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For: Laurin C, Boomsma D, Lubke G. The use of vector bootstrapping to improve variable selection precision in Lasso models. Stat Appl Genet Mol Biol 2016;15:305-20. [PMID: 27248122 PMCID: PMC5131926 DOI: 10.1515/sagmb-2015-0043] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]

For:	Laurin C, Boomsma D, Lubke G. The use of vector bootstrapping to improve variable selection precision in Lasso models. Stat Appl Genet Mol Biol 2016;15:305-20. [PMID: 27248122 PMCID: PMC5131926 DOI: 10.1515/sagmb-2015-0043] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]

Number

Cited by Other Article(s)

Meade RD, Akerman AP, Notley SR, Kirby NV, Sigal RJ, Kenny GP. Exploring the contribution of inter-individual factors to the development of physiological heat strain in older adults exposed to simulated indoor overheating. Appl Physiol Nutr Metab 2024;49:1252-1270. [PMID: 38830263 DOI: 10.1139/apnm-2024-0135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]

Lin HY, Mazumder H, Sarkar I, Huang PY, Eeles RA, Kote-Jarai Z, Muir KR, Schleutker J, Pashayan N, Batra J, Neal DE, Nielsen SF, Nordestgaard BG, Grönberg H, Wiklund F, MacInnis RJ, Haiman CA, Travis RC, Stanford JL, Kibel AS, Cybulski C, Khaw KT, Maier C, Thibodeau SN, Teixeira MR, Cannon-Albright L, Brenner H, Kaneva R, Pandha H, Park JY. Cluster effect for SNP-SNP interaction pairs for predicting complex traits. Sci Rep 2024;14:18677. [PMID: 39134575 PMCID: PMC11319716 DOI: 10.1038/s41598-024-66311-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 07/01/2024] [Indexed: 08/15/2024] Open

Affiliation(s)

Hui-Yi Lin Biostatistics and Data Science Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, 70112, USA.
Harun Mazumder Biostatistics and Data Science Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, 70112, USA
Indrani Sarkar Biostatistics and Data Science Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, 70112, USA
Po-Yu Huang Information and Communications Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan
Rosalind A Eeles The Institute of Cancer Research, London, SM2 5NG, UK Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK
Zsofia Kote-Jarai The Institute of Cancer Research, London, SM2 5NG, UK
Kenneth R Muir Division of Population Health, Health Services Research and Primary Care, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
Johanna Schleutker Institute of Biomedicine, University of Turku, Turku, Finland Department of Medical Genetics, Genomics, Laboratory Division, Turku University Hospital, PO Box 52, 20521, Turku, Finland
Nora Pashayan Department of Applied Health Research, University College London, London, WC1E 7HB, UK Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge, CB1 8RN, UK
Jyotsna Batra Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, QLD, 4059, Australia Translational Research Institute, Brisbane, QLD, 4102, Australia
David E Neal Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Room 6603, Level 6, Headley Way, Headington, Oxford, OX3 9DU, UK Department of Oncology, University of Cambridge, Addenbrooke's Hospital, Hills Road, Box 279, Cambridge, CB2 0QQ, UK Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Cambridge, CB2 0RE, UK
Sune F Nielsen Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, 2200, Copenhagen, Denmark
Børge G Nordestgaard Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, 2200, Copenhagen, Denmark
Henrik Grönberg Department of Medical Epidemiology and Biostatistics, Karolinska Institute, 171 77, Stockholm, Sweden
Fredrik Wiklund Department of Medical Epidemiology and Biostatistics, Karolinska Institute, 171 77, Stockholm, Sweden
Robert J MacInnis Cancer Epidemiology Division, Cancer Council Victoria, 200 Victoria Parade, East Melbourne, 3002, Australia Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia
Christopher A Haiman Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90015, USA
Ruth C Travis Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
Janet L Stanford Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, 98109-1024, USA Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, 98195, USA
Adam S Kibel Division of Urologic Surgery, Brigham and Womens Hospital, 75 Francis Street, Boston, MA, 02115, USA
Cezary Cybulski International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, 70-115, Szczecin, Poland
Kay-Tee Khaw Clinical Gerontology Unit, University of Cambridge, Cambridge, CB2 2QQ, UK
Christiane Maier Humangenetik Tuebingen, Paul-Ehrlich-Str 23, 72076, Tuebingen, Germany
Stephen N Thibodeau Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
Manuel R Teixeira Department of Laboratory Genetics, Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center, Porto, Portugal Cancer Genetics Group, IPO Porto Research Center (CI-IPOP)/RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center, Porto, Portugal School of Medicine and Biomedical Sciences (ICBAS), University of Porto, Porto, Portugal
Lisa Cannon-Albright Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, 84132, USA George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, 84148, USA
Hermann Brenner Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
Radka Kaneva Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, 2 Zdrave Str., 1431, Sofia, Bulgaria
Hardev Pandha The University of Surrey, Guildford, Surrey, GU2 7XH, UK
Jong Y Park Department of Cancer Epidemiology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL, 33612, USA

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Schupp PG, Shelton SJ, Brody DJ, Eliscu R, Johnson BE, Mazor T, Kelley KW, Potts MB, McDermott MW, Huang EJ, Lim DA, Pieper RO, Berger MS, Costello JF, Phillips JJ, Oldham MC. Deconstructing Intratumoral Heterogeneity through Multiomic and Multiscale Analysis of Serial Sections. Cancers (Basel) 2024;16:2429. [PMID: 39001492 PMCID: PMC11240479 DOI: 10.3390/cancers16132429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Revised: 06/27/2024] [Accepted: 06/28/2024] [Indexed: 07/16/2024] Open

Affiliation(s)

Patrick G. Schupp Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.) Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA
Samuel J. Shelton Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
Daniel J. Brody Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
Rebecca Eliscu Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
Brett E. Johnson Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
Tali Mazor Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.) Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA
Kevin W. Kelley Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.) Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA 94143, USA Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA
Matthew B. Potts Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
Michael W. McDermott Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
Eric J. Huang Department of Pathology, University of California, San Francisco, San Francisco, CA 94143, USA;
Daniel A. Lim Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
Russell O. Pieper Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
Mitchel S. Berger Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
Joseph F. Costello Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
Joanna J. Phillips Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.) Department of Pathology, University of California, San Francisco, San Francisco, CA 94143, USA;
Michael C. Oldham Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)

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Li M, Hu X, Li Y, Chen G, Ding CG, Tian X, Tian P, Xiang H, Pan X, Ding X, Xue W, Zheng J. Development and validation of a novel nomogram model for predicting delayed graft function in deceased donor kidney transplantation based on pre-transplant biopsies. BMC Nephrol 2024;25:138. [PMID: 38641807 PMCID: PMC11031976 DOI: 10.1186/s12882-024-03557-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 03/21/2024] [Indexed: 04/21/2024] Open

Abstract

BACKGROUND

Delayed graft function (DGF) is an important complication after kidney transplantation surgery. The present study aimed to develop and validate a nomogram for preoperative prediction of DGF on the basis of clinical and histological risk factors.

METHODS

The prediction model was constructed in a development cohort comprising 492 kidney transplant recipients from May 2018 to December 2019. Data regarding donor and recipient characteristics, pre-transplantation biopsy results, and machine perfusion parameters were collected, and univariate analysis was performed. The least absolute shrinkage and selection operator regression model was used for variable selection. The prediction model was developed by multivariate logistic regression analysis and presented as a nomogram. An external validation cohort comprising 105 transplantation cases from January 2020 to April 2020 was included in the analysis.

RESULTS

266 donors were included in the development cohort, 458 kidneys (93.1%) were preserved by hypothermic machine perfusion (HMP), 96 (19.51%) of 492 recipients developed DGF. Twenty-eight variables measured before transplantation surgery were included in the LASSO regression model. The nomogram consisted of 12 variables from donor characteristics, pre-transplantation biopsy results and machine perfusion parameters. Internal and external validation showed good discrimination and calibration of the nomogram, with Area Under Curve (AUC) 0.83 (95%CI, 0.78-0.88) and 0.87 (95%CI, 0.80-0.94). Decision curve analysis demonstrated that the nomogram was clinically useful.

CONCLUSION

A DGF predicting nomogram was developed that incorporated donor characteristics, pre-transplantation biopsy results, and machine perfusion parameters. This nomogram can be conveniently used for preoperative individualized prediction of DGF in kidney transplant recipients.

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Affiliation(s)

Meihe Li Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
Xiaojun Hu Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
Yang Li Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
Guozhen Chen Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
Chen-Guang Ding Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
Xiaohui Tian Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
Puxun Tian Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
Heli Xiang Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
Xiaoming Pan Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
Xiaoming Ding Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
Wujun Xue Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China.
Jin Zheng Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China.

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Schupp PG, Shelton SJ, Brody DJ, Eliscu R, Johnson BE, Mazor T, Kelley KW, Potts MB, McDermott MW, Huang EJ, Lim DA, Pieper RO, Berger MS, Costello JF, Phillips JJ, Oldham MC. Deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial sections. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.21.545365. [PMID: 37645893 PMCID: PMC10461981 DOI: 10.1101/2023.06.21.545365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]

Affiliation(s)

Patrick G. Schupp Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, California, USA
Samuel J. Shelton Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
Daniel J. Brody Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
Rebecca Eliscu Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
Brett E. Johnson Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
Tali Mazor Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, California, USA Medical Scientist Training Program and Neuroscience Graduate Program, University of California San Francisco, San Francisco, California, USA
Kevin W. Kelley Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA Medical Scientist Training Program and Neuroscience Graduate Program, University of California San Francisco, San Francisco, California, USA Neuroscience Graduate Program, University of California San Francisco, San Francisco, California, USA
Matthew B. Potts Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
Michael W. McDermott Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
Eric J. Huang Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
Daniel A. Lim Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
Russell O. Pieper Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
Mitchel S. Berger Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
Joseph F. Costello Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
Joanna J. Phillips Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA Department of Pathology, University of California, San Francisco, San Francisco, California, USA
Michael C. Oldham Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA

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Meester M, Tobias TJ, van den Broek J, Meulenbroek CB, Bouwknegt M, van der Poel WH, Stegeman A. Farm biosecurity measures to prevent hepatitis E virus infection in finishing pigs on endemically infected pig farms. One Health 2023;16:100570. [PMID: 37363225 PMCID: PMC10288132 DOI: 10.1016/j.onehlt.2023.100570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 05/17/2023] [Accepted: 05/23/2023] [Indexed: 06/28/2023] Open

Abstract

Hepatitis E virus (HEV) can be transmitted from pigs to humans and cause liver inflammation. Pigs are a major reservoir of HEV and most slaughter pigs show evidence of infection by presence of antibodies (ELISA) or viral RNA (PCR). Reducing the number of HEV infected pigs at slaughter would likely reduce human exposure, yet how this can be achieved, is unknown. We aimed to identify and quantify the effect of biosecurity measures to deliver HEV negative batches of pigs to slaughter. A case-control study was performed with Dutch pig farms selected based on results of multiple slaughter batches. Case farms delivered at least one PCR and ELISA negative batch to slaughter (PCR^-ELISA^-), indicating absence of HEV infection, and control farms had the highest proportion of PCR and/or ELISA positive batches (PCR⁺ELISA⁺), indicating high within-farm transmission. Data about biosecurity and housing were collected via a questionnaire and an audit. Variables were selected by regularization (LASSO regression) and ranked, based the frequency of variable selection. The odds ratios (OR) for the relation between case-control status and the highest ranked variables were determined via grouped logistic regression. Thirty-five case farms, with 10 to 60% PCR^-ELISA^- batches, and 38 control farms with on average 40% PCR⁺ELISA⁺ batches, were included. Rubber and steel floor material in fattening pens had the highest ranking and increased the odds of a PCR^-ELISA^- batch by 5.87 (95%CI 3.03-11.6) and 7.13 (95%CI 3.05-16.9) respectively. Cleaning pig driving boards weekly (OR 1.99 (95%CI 1.07-3.80)), and fly control with predatory flies (OR 4.52 (95%CI 1.59-13.5)) were protective, whereas a long fattening period was a risk. This study shows that cleaning and cleanability of floors and fomites and adequate fly control are measures to consider for HEV control in infected farms. Yet, intervention studies are needed to confirm the robustness of these outcomes.

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Lin HY, Steck SE, Sarkar I, Fontham ETH, Diekman A, Rogers LJ, Ratliff CT, Bensen JT, Mohler JL, Su LJ. Interactions of SNPs in Folate Metabolism Related Genes on Prostate Cancer Aggressiveness in European Americans and African Americans. Cancers (Basel) 2023;15:cancers15061699. [PMID: 36980585 PMCID: PMC10046243 DOI: 10.3390/cancers15061699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 03/12/2023] Open

Kim S, Capasso A, Ali SH, Headley T, DiClemente RJ, Tozan Y. What predicts people's belief in COVID-19 misinformation? A retrospective study using a nationwide online survey among adults residing in the United States. BMC Public Health 2022;22:2114. [PMID: 36401186 PMCID: PMC9673212 DOI: 10.1186/s12889-022-14431-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 10/24/2022] [Indexed: 11/19/2022] Open

Genes, exposures, and interactions on preterm birth risk: an exploratory study in an Argentine population. J Community Genet 2022;13:557-565. [PMID: 35976607 DOI: 10.1007/s12687-022-00605-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 08/12/2022] [Indexed: 10/15/2022] Open

Zamanzadeh A, Cavoli T. The effect of nonpharmaceutical interventions on COVID-19 infections for lower and middle-income countries: A debiased LASSO approach. PLoS One 2022;17:e0271586. [PMID: 35867692 PMCID: PMC9307185 DOI: 10.1371/journal.pone.0271586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 07/05/2022] [Indexed: 11/18/2022] Open

Sutton M, Sugier PE, Truong T, Liquet B. Leveraging pleiotropic association using sparse group variable selection in genomics data. BMC Med Res Methodol 2022;22:9. [PMID: 34996381 PMCID: PMC8742466 DOI: 10.1186/s12874-021-01491-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 12/03/2021] [Indexed: 12/04/2022] Open

Zhang P, Liu Z, Wang D, Li Y, Xing Y, Xiao Y. Scoring System Based on RNA Modification Writer-Related Genes to Predict Overall Survival and Therapeutic Response in Bladder Cancer. Front Immunol 2021;12:724541. [PMID: 34512654 PMCID: PMC8427805 DOI: 10.3389/fimmu.2021.724541] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 08/09/2021] [Indexed: 02/05/2023] Open

Poletti S, Mazza MG, Calesella F, Vai B, Lorenzi C, Manfredi E, Colombo C, Zanardi R, Benedetti F. Circulating inflammatory markers impact cognitive functions in bipolar depression. J Psychiatr Res 2021;140:110-116. [PMID: 34107379 DOI: 10.1016/j.jpsychires.2021.05.071] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/05/2021] [Accepted: 05/29/2021] [Indexed: 11/25/2022]

Cavicchioli M, Calesella F, Cazzetta S, Mariagrazia M, Ogliari A, Maffei C, Vai B. Investigating predictive factors of dialectical behavior therapy skills training efficacy for alcohol and concurrent substance use disorders: A machine learning study. Drug Alcohol Depend 2021;224:108723. [PMID: 33965687 DOI: 10.1016/j.drugalcdep.2021.108723] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 02/22/2021] [Accepted: 03/17/2021] [Indexed: 01/06/2023]

Abstract

BACKGROUND

Dialectical Behavior Therapy Skills Training (DBT-ST) as stand-alone treatment has demonstrated promising outcomes for the treatment of alcohol use disorder (AUD) and concurrent substance use disorders (SUDs). However, no studies have so far empirically investigated factors that might predict efficacy of this therapeutic model.

METHODS

275 treatment-seeking individuals with AUD and other SUDs were consecutively admitted to a 3-month DBT-ST program (in- + outpatient; outpatient settings). The machine learning routine applied (i.e. penalized regression combined with a nested cross-validation procedure) was conducted in order to estimate predictive values of a wide panel of clinical variables in a single statistical framework on drop-out and substance-use behaviors, dealing with related multicollinearity, and eliminating redundant variables.

RESULTS

The cross-validated elastic net model significantly predicted the drop-out. The bootstrap analysis revealed that subjects who showed substance-use behaviors during the intervention and who were treated with the mixed setting (i.e., in- and outpatient) program, together with higher ASI alcohol scores were associated with an higher probability of drop-out. On the contrary, older subjects, higher levels of education, together with higher scores of DERS awareness subscale were negatively associated to drop-out. Similarly, lifetime co-diagnoses of anxiety, bipolar, and gambling disorders, together with bulimia nervosa negatively predicted the drop-out. The machine learning model did not identify predictive variables of substance-use behaviors during the treatment.

CONCLUSIONS

The DBT-ST program could be considered a valid therapeutic approach especially when AUD and other SUDs co-occur with other psychiatric conditions and, it is carried out as a full outpatient intervention.

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A peripheral inflammatory signature discriminates bipolar from unipolar depression: A machine learning approach. Prog Neuropsychopharmacol Biol Psychiatry 2021;105:110136. [PMID: 33045321 DOI: 10.1016/j.pnpbp.2020.110136] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/04/2020] [Accepted: 10/06/2020] [Indexed: 12/11/2022]

Aleksandrova K, Reichmann R, Kaaks R, Jenab M, Bueno-de-Mesquita HB, Dahm CC, Eriksen AK, Tjønneland A, Artaud F, Boutron-Ruault MC, Severi G, Hüsing A, Trichopoulou A, Karakatsani A, Peppa E, Panico S, Masala G, Grioni S, Sacerdote C, Tumino R, Elias SG, May AM, Borch KB, Sandanger TM, Skeie G, Sánchez MJ, Huerta JM, Sala N, Gurrea AB, Quirós JR, Amiano P, Berntsson J, Drake I, van Guelpen B, Harlid S, Key T, Weiderpass E, Aglago EK, Cross AJ, Tsilidis KK, Riboli E, Gunter MJ. Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score. BMC Med 2021;19:1. [PMID: 33390155 PMCID: PMC7780676 DOI: 10.1186/s12916-020-01826-0] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 10/23/2020] [Indexed: 12/24/2022] Open

Abstract

BACKGROUND

Nutrition and lifestyle have been long established as risk factors for colorectal cancer (CRC). Modifiable lifestyle behaviours bear potential to minimize long-term CRC risk; however, translation of lifestyle information into individualized CRC risk assessment has not been implemented. Lifestyle-based risk models may aid the identification of high-risk individuals, guide referral to screening and motivate behaviour change. We therefore developed and validated a lifestyle-based CRC risk prediction algorithm in an asymptomatic European population.

METHODS

The model was based on data from 255,482 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) study aged 19 to 70 years who were free of cancer at study baseline (1992-2000) and were followed up to 31 September 2010. The model was validated in a sample comprising 74,403 participants selected among five EPIC centres. Over a median follow-up time of 15 years, there were 3645 and 981 colorectal cancer cases in the derivation and validation samples, respectively. Variable selection algorithms in Cox proportional hazard regression and random survival forest (RSF) were used to identify the best predictors among plausible predictor variables. Measures of discrimination and calibration were calculated in derivation and validation samples. To facilitate model communication, a nomogram and a web-based application were developed.

RESULTS

The final selection model included age, waist circumference, height, smoking, alcohol consumption, physical activity, vegetables, dairy products, processed meat, and sugar and confectionary. The risk score demonstrated good discrimination overall and in sex-specific models. Harrell's C-index was 0.710 in the derivation cohort and 0.714 in the validation cohort. The model was well calibrated and showed strong agreement between predicted and observed risk. Random survival forest analysis suggested high model robustness. Beyond age, lifestyle data led to improved model performance overall (continuous net reclassification improvement = 0.307 (95% CI 0.264-0.352)), and especially for young individuals below 45 years (continuous net reclassification improvement = 0.364 (95% CI 0.084-0.575)).

CONCLUSIONS

LiFeCRC score based on age and lifestyle data accurately identifies individuals at risk for incident colorectal cancer in European populations and could contribute to improved prevention through motivating lifestyle change at an individual level.

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Affiliation(s)

Krasimira Aleksandrova Nutrition, Immunity and Metabolism Senior Scientist Group, Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany. Institute of Nutritional Science, University of Potsdam, Potsdam, Germany. Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany.
Robin Reichmann Nutrition, Immunity and Metabolism Senior Scientist Group, Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany Institute of Nutritional Science, University of Potsdam, Potsdam, Germany
Rudolf Kaaks Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
Mazda Jenab International Agency for Research on Cancer, World Health Organization, Lyon, France
H Bas Bueno-de-Mesquita National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
Christina C Dahm Department of Public Health, Aarhus University, Aarhus, Denmark
Anne Kirstine Eriksen Danish Cancer Society Research Center, Copenhagen, Denmark
Anne Tjønneland Danish Cancer Society Research Center, Copenhagen, Denmark
Fanny Artaud CESP, Faculté de Medicine, Université Paris-Saclay, Villejuif, France Institut Gustave Roussy, Villejuif, France
Marie-Christine Boutron-Ruault CESP, Faculté de Medicine, Université Paris-Saclay, Villejuif, France Institut Gustave Roussy, Villejuif, France
Gianluca Severi CESP, Faculté de Medicine, Université Paris-Saclay, Villejuif, France Institut Gustave Roussy, Villejuif, France Dipartimento di Statistica, Informatica e Applicazioni "G. Parenti" (DISIA), University of Florence, Florence, Italy
Anika Hüsing Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
Antonia Trichopoulou Hellenic Health Foundation, Athens, Greece
Anna Karakatsani Hellenic Health Foundation, Athens, Greece 2nd Pulmonary Medicine Department, School of Medicine, National and Kapodistrian University of Athens, "ATTIKON" University Hospital, Haidari, Greece
Eleni Peppa Hellenic Health Foundation, Athens, Greece
Salvatore Panico EPIC Centre of Naples, Dipartimento di Medicina Clinica e Chirurgia, University of Naples Federico II, Naples, Italy
Giovanna Masala Cancer Risk Factors and Lifestyle Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network - ISPRO, Florence, Italy
Sara Grioni Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
Carlotta Sacerdote Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital and Center for Cancer Prevention (CPO), Turin, Italy
Rosario Tumino Cancer Registry and Histopathology Department, Provincial Health Authority (ASP), Ragusa, Italy
Sjoerd G Elias Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
Anne M May Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
Kristin B Borch Department of Community Medicine, Health Faculty, UiT-the Arctic university of Norway, Tromsø, Norway
Torkjel M Sandanger Department of Community Medicine, Health Faculty, UiT-the Arctic university of Norway, Tromsø, Norway
Guri Skeie Department of Community Medicine, Health Faculty, UiT-the Arctic university of Norway, Tromsø, Norway
Maria-Jose Sánchez Escuela Andaluza de Salud Pública (EASP), Granada, Spain Instituto de Investigación Biosanitaria ibs. GRANADA, Granada, Spain Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Universidad de Granada, Granada, Spain
José María Huerta Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain
Núria Sala Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Translational Research Laboratory, Catalan Institute of Oncology (ICO), Barcelona, Spain Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
Aurelio Barricarte Gurrea Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Navarra Public Health Institute, Pamplona, Spain Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
José Ramón Quirós Public Health Directorate, Asturias, Spain
Pilar Amiano Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Ministry of Health of the Basque Government, Public Health Division of Gipuzkoa, Biodonostia Health Research Institute, Donostia-San Sebastian, Spain
Jonna Berntsson Department of Clinical Sciences, Division of Oncology and Pathology, Lund University, Lund, Sweden
Isabel Drake Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
Bethany van Guelpen Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
Sophia Harlid Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
Tim Key Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
Elisabete Weiderpass International Agency for Research on Cancer, World Health Organization, Lyon, France
Elom K Aglago International Agency for Research on Cancer, World Health Organization, Lyon, France
Amanda J Cross Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
Konstantinos K Tsilidis Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
Elio Riboli Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
Marc J Gunter International Agency for Research on Cancer, World Health Organization, Lyon, France

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Benedetti F, Poletti S, Vai B, Mazza MG, Lorenzi C, Brioschi S, Aggio V, Branchi I, Colombo C, Furlan R, Zanardi R. Higher baseline interleukin-1β and TNF-α hamper antidepressant response in major depressive disorder. Eur Neuropsychopharmacol 2021;42:35-44. [PMID: 33191075 DOI: 10.1016/j.euroneuro.2020.11.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 10/18/2020] [Accepted: 11/06/2020] [Indexed: 01/06/2023]

Affiliation(s)

Francesco Benedetti Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy; Vita-Salute San Raffaele University, Milano, Italy.
Sara Poletti Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy; Vita-Salute San Raffaele University, Milano, Italy
Benedetta Vai Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy; Vita-Salute San Raffaele University, Milano, Italy; Fondazione Centro San Raffaele, Milano, Italy
Mario Gennaro Mazza Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy; Vita-Salute San Raffaele University, Milano, Italy
Cristina Lorenzi Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy
Silvia Brioschi Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy
Veronica Aggio Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy; Vita-Salute San Raffaele University, Milano, Italy
Igor Branchi Center for Behavioral Sciences and Mental Health, Istituto Superiore di Sanità, Rome, Italy
Cristina Colombo Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy; Vita-Salute San Raffaele University, Milano, Italy
Roberto Furlan Vita-Salute San Raffaele University, Milano, Italy; Clinical Neuroimmunology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy
Raffaella Zanardi Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy; Vita-Salute San Raffaele University, Milano, Italy

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Schürch CM, Bhate SS, Barlow GL, Phillips DJ, Noti L, Zlobec I, Chu P, Black S, Demeter J, McIlwain DR, Kinoshita S, Samusik N, Goltsev Y, Nolan GP. Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front. Cell 2020;182:1341-1359.e19. [PMID: 32763154 PMCID: PMC7479520 DOI: 10.1016/j.cell.2020.07.005] [Citation(s) in RCA: 363] [Impact Index Per Article: 90.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 04/22/2020] [Accepted: 07/08/2020] [Indexed: 12/21/2022]

Affiliation(s)

Christian M Schürch Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Salil S Bhate Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University School of Medicine, Stanford, CA 94305, USA
Graham L Barlow Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
Darci J Phillips Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Dermatology, Stanford University School of Medicine, Stanford, CA 94305, USA
Luca Noti Institute of Pathology, University of Bern, 3008 Bern, Switzerland
Inti Zlobec Institute of Pathology, University of Bern, 3008 Bern, Switzerland
Pauline Chu Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
Sarah Black Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
Janos Demeter Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
David R McIlwain Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
Shigemi Kinoshita Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
Nikolay Samusik Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
Yury Goltsev Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
Garry P Nolan Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.

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Herzog P, Voderholzer U, Gärtner T, Osen B, Svitak M, Doerr R, Rolvering-Dijkstra M, Feldmann M, Rief W, Brakemeier EL. Predictors of outcome during inpatient psychotherapy for posttraumatic stress disorder: a single-treatment, multi-site, practice-based study. Psychother Res 2020;31:468-482. [PMID: 32762508 DOI: 10.1080/10503307.2020.1802081] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open

Jacobucci R, Brandmaier AM, Kievit RA. A Practical Guide to Variable Selection in Structural Equation Models with Regularized MIMIC Models. ADVANCES IN METHODS AND PRACTICES IN PSYCHOLOGICAL SCIENCE 2019;2:55-76. [PMID: 31463424 PMCID: PMC6713564 DOI: 10.1177/2515245919826527] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]

Jiang Z, Dreyer RP, Spertus JA, Masoudi FA, Li J, Zheng X, Li X, Wu C, Bai X, Hu S, Wang Y, Krumholz HM, Chen H. Factors Associated With Return to Work After Acute Myocardial Infarction in China. JAMA Netw Open 2018;1:e184831. [PMID: 30646375 PMCID: PMC6324382 DOI: 10.1001/jamanetworkopen.2018.4831] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open

Abstract

IMPORTANCE

Return to work is an important indicator of recovery after acute myocardial infarction. Little is known, however, about the rate of returning to work within the year after an acute myocardial infarction in China, as well as the factors associated with returning to work after an acute myocardial infarction.

OBJECTIVES

To determine the rate of return to work within 12 months after acute myocardial infarction, classify the reasons why patients did not return to work, and identify patient factors associated with returning to work.

DESIGN, SETTING, AND PARTICIPANTS

This prospective cohort study, conducted in 53 hospitals across 21 provinces in China, identified 1566 patients who were employed at the time of the index acute myocardial infarction hospitalization and participating in the China Patient-centered Evaluative Assessment of Cardiac Events Prospective Study of Acute Myocardial Infarction. Data collected included patients' baseline characteristics; employment status at 12 months after acute myocardial infarction; and, for those who were not employed at 12 months, potential reasons for not returning to work. A logistic regression model was fitted to identify factors associated with returning to work at 12 months. Data were collected from January 1, 2013, through July 17, 2014, and statistical analysis was conducted from August 9, 2016, to August 15, 2018.

MAIN OUTCOMES AND MEASURES

Return to work, defined as rejoining the workforce within 12 months after discharge from hospitalization for the index acute myocardial infarction.

RESULTS

Of 1566 patients (130 women and 1436 men; mean [SD] age, 52.2 [9.7] years), 875 patients (55.9%; 95% CI, 53.4%-58.3%) returned to work by 12 months after acute myocardial infarction. Among the 691 patients who did not return to work, 287 (41.5%) were unable to work and/or preferred not to work because of acute myocardial infarction and 131 (19.0%) retired early owing to the acute myocardial infarction. Female sex (relative risk, 0.65; 95% CI, 0.41-0.88), a history of smoking (relative risk, 0.82; 95% CI, 0.65-0.98), and in-hospital complications during the index acute myocardial infarction (relative risk, 0.96; 95% CI, 0.93-0.99) were associated with a lower likelihood of returning to work.

CONCLUSIONS AND RELEVANCE

Almost half of the previously employed Chinese patients did not return to work within 12 months after acute myocardial infarction. Female sex, history of smoking, and in-hospital complications were associated with a lower likelihood of returning to work.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01624909.

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Affiliation(s)

Zihan Jiang Health Care and International Medical Services, Peking Union Medical College Hospital, Beijing, People’s Republic of China
Rachel P. Dreyer Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut Center for Outcomes Research and Evaluation, Yale–New Haven Hospital, New Haven, Connecticut
John A. Spertus Department of Biomedical and Health Informatics, University of Missouri–Kansas City Department of Cardiovascular Research, St Luke’s Mid America Heart Institute, Kansas City, Missouri
Frederick A. Masoudi Department of Medicine, University of Colorado School of Medicine at the Anschutz Medical Campus, Aurora Colorado Cardiovascular Outcomes Research Consortium, Denver
Jing Li National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
Xin Zheng National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
Xi Li National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
Chaoqun Wu National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
Xueke Bai National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
Shuang Hu National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
Yun Wang Center for Outcomes Research and Evaluation, Yale–New Haven Hospital, New Haven, Connecticut Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
Harlan M. Krumholz Center for Outcomes Research and Evaluation, Yale–New Haven Hospital, New Haven, Connecticut Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut
Hong Chen Department of Cardiology, Peking University People’s Hospital, Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Beijing, People’s Republic of China

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Burke TA, Jacobucci R, Ammerman BA, Piccirillo M, McCloskey MS, Heimberg RG, Alloy LB. Identifying the relative importance of non-suicidal self-injury features in classifying suicidal ideation, plans, and behavior using exploratory data mining. Psychiatry Res 2018;262:175-183. [PMID: 29453036 PMCID: PMC6684203 DOI: 10.1016/j.psychres.2018.01.045] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 11/29/2017] [Accepted: 01/24/2018] [Indexed: 10/18/2022]