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Rout M, Tung GK, Singh JR, Mehra NK, Wander GS, Ralhan S, Sanghera DK. Polygenic Risk Score Assessment for Coronary Artery Disease in Asian Indians. J Cardiovasc Transl Res 2024:10.1007/s12265-024-10511-z. [PMID: 38658478 DOI: 10.1007/s12265-024-10511-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/11/2024] [Indexed: 04/26/2024]
Abstract
We evaluated the performance of various polygenic risk score (PRS) models derived from European (EU), South Asian (SA), and Punjabi Asian Indians (AI) studies on 13,974 subjects from AI ancestry. While all models successfully predicted Coronary artery disease (CAD) risk, the AI, SA, and EU + AI were superior predictors and more transportable than the EU model; the predictive performance in training and test sets was 18% and 22% higher in AI and EU + AI models, respectively than in EU. Comparing individuals with extreme PRS quartiles, the AI and EU + AI captured individuals with high CAD risk showed 2.6 to 4.6 times higher efficiency than the EU. Interestingly, including the clinical risk score did not significantly change the performance of any genetic model. The enrichment of diversity variants in EU PRS improves risk prediction and transportability. Establishing population-specific normative and risk factors and inclusion into genetic models would refine the risk stratification and improve the clinical utility of CAD PRS.
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Affiliation(s)
- Madhusmita Rout
- Department of Pediatrics, Section of Genetics, College of Medicine, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd., Rm 317 BMSB, Oklahoma City, OK, 73104, USA
| | - Gurleen Kaur Tung
- Department of Pediatrics, Section of Genetics, College of Medicine, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd., Rm 317 BMSB, Oklahoma City, OK, 73104, USA
| | | | | | | | - Sarju Ralhan
- Hero DMC Heart Institute, Ludhiana, Punjab, India
| | - Dharambir K Sanghera
- Department of Pediatrics, Section of Genetics, College of Medicine, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd., Rm 317 BMSB, Oklahoma City, OK, 73104, USA.
- Department of Pharmaceutical Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
- Department of Physiology, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
- Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
- Harold Hamm Diabetes Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
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Balikov DA, Zhou Y, Miller JML. A Telephone Triage System for Patients Calling with Symptoms of a Posterior Vitreous Detachment. Ophthalmol Retina 2023:S2468-6530(23)00003-9. [PMID: 36634817 DOI: 10.1016/j.oret.2023.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 01/11/2023]
Abstract
PURPOSE The purpose of the study was to develop a simple telephone questionnaire, without physical examination input, that predicts which patients calling with symptoms of a posterior vitreous detachment (PVD) have a retinal tear (RT) or rhegmatogenous retinal detachment (RD). DESIGN Prospective cohort (quality improvement) study. PARTICIPANTS All patients with symptoms consistent with a PVD calling a major academic ophthalmology department over a 4-month period in 2020 and who were seen on follow-up within 1.5 months (211 screened and 193 included). METHODS A comprehensive telephone questionnaire assessing for RT/RD risk factors was administered by telephone triage staff to all patients calling with symptoms of flashes, floaters, or curtain/veil in their vision. Multivariable logistic regression was used to determine risk factors most predictive of having an RT/RD during the add-on visit. Risk factor odds ratios were used to develop an RT/RD risk score. MAIN OUTCOMES MEASURES Development of a clinical risk score for having an RT/RD at the add-on visit after telephone triage. RESULTS Approximately 55% of patients were previously established in the retina clinic, 26% were new to the department, 19% were previously established in the comprehensive clinic, and 7% had an RT/RD at the add-on visit. Out of 23 questions and 70 prespecified possible answers from the telephone questionnaire, the final clinical risk score for RT/RDs was derived from 7 questions and 15 possible answers. The simplified questionnaire can be administered quickly by telephone operators without any reference to physical examination or the patient's chart. The receive-operator curve for our final multivariable logistic regression and clinical risk score models have an area under the curve of > 0.90. Using a conservative clinical risk score, approximately 50% of all patients without an RT/RD can be safely seen nonurgently. Progressively higher scores can be used to determine relative urgency of an appointment. CONCLUSIONS To our knowledge, this is the first study to predict risk of an RT/RD in a patient calling with symptoms consistent with a PVD without reference to the patient's physical examination or chart. Our clinical risk scoring system can be used to determine urgency of an add-on appointment and increase the number of low-risk patients with symptomatic PVDs who are scheduled routinely. FINANCIAL DISCLOSURE(S) The author(s) have no proprietary or commercial interest in any materials discussed in this article.
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Lu X, Liu Z, Cui Q, Liu F, Li J, Niu X, Shen C, Hu D, Huang K, Chen J, Xing X, Zhao Y, Lu F, Liu X, Cao J, Chen S, Ma H, Yu L, Wu X, Wu X, Li Y, Zhang H, Mo X, Zhao L, Huang J, Wang L, Wen W, Shu XO, Takeuchi F, Koh WP, Tai ES, Cheng CY, Wong TY, Chang X, Chan MYY, Gao W, Zheng H, Chen K, Chen J, He J, Tang CSM, Lam KSL, Tse HF, Cheung CYY, Takahashi A, Kubo M, Kato N, Terao C, Kamatani Y, Sham PC, Heng CK, Hu Z, Chen YE, Wu T, Shen H, Willer CJ, Gu D. A polygenic risk score improves risk stratification of coronary artery disease: a large-scale prospective Chinese cohort study. Eur Heart J 2022; 43:1702-1711. [PMID: 35195259 PMCID: PMC9076396 DOI: 10.1093/eurheartj/ehac093] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 11/22/2021] [Accepted: 02/14/2022] [Indexed: 12/15/2022] Open
Abstract
Aims To construct a polygenic risk score (PRS) for coronary artery disease (CAD) and comprehensively evaluate its potential in clinical utility for primary prevention in Chinese populations. Methods and results Using meta-analytic approach and large genome-wide association results for CAD and CAD-related traits in East Asians, a PRS comprising 540 genetic variants was developed in a training set of 2800 patients with CAD and 2055 controls, and was further assessed for risk stratification for CAD integrating with the guideline-recommended clinical risk score in large prospective cohorts comprising 41 271 individuals. During a mean follow-up of 13.0 years, 1303 incident CAD cases were identified. Individuals with high PRS (the highest 20%) had about three-fold higher risk of CAD than the lowest 20% (hazard ratio 2.91, 95% confidence interval 2.43–3.49), with the lifetime risk of 15.9 and 5.8%, respectively. The addition of PRS to the clinical risk score yielded a modest yet significant improvement in C-statistic (1%) and net reclassification improvement (3.5%). We observed significant gradients in both 10-year and lifetime risk of CAD according to the PRS within each clinical risk strata. Particularly, when integrating high PRS, intermediate clinical risk individuals with uncertain clinical decision for intervention would reach the risk levels (10-year of 4.6 vs. 4.8%, lifetime of 17.9 vs. 16.6%) of high clinical risk individuals with intermediate (20–80%) PRS. Conclusion The PRS could stratify individuals into different trajectories of CAD risk, and further refine risk stratification for CAD within each clinical risk strata, demonstrating a great potential to identify high-risk individuals for targeted intervention in clinical utility.
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Affiliation(s)
- Xiangfeng Lu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Zhongying Liu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Qingmei Cui
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Fangchao Liu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Jianxin Li
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Xiaoge Niu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Chong Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Dongsheng Hu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China.,Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen 518071, China
| | - Keyong Huang
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Jichun Chen
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Xiaolong Xing
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Yingxin Zhao
- Cardio-Cerebrovascular Control and Research Center, Institute of Basic Medicine, Shandong Academy of Medical Sciences, Jinan 250062, China
| | - Fanghong Lu
- Cardio-Cerebrovascular Control and Research Center, Institute of Basic Medicine, Shandong Academy of Medical Sciences, Jinan 250062, China
| | - Xiaoqing Liu
- Division of Epidemiology, Guangdong Provincial People's Hospital and Cardiovascular Institute, Guangzhou 510080, China
| | - Jie Cao
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Shufeng Chen
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Ling Yu
- Department of Cardiology, Fujian Provincial People's Hospital, Fuzhou 350014, China
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu 610041, China
| | - Xigui Wu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Ying Li
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Huan Zhang
- Center for Genetic Epidemiology and Genomics, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou 215123, China
| | - Xingbo Mo
- Center for Genetic Epidemiology and Genomics, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou 215123, China
| | - Liancheng Zhao
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Jianfeng Huang
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Laiyuan Wang
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
| | - Wanqing Wen
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiao-Ou Shu
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Woon-Puay Koh
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore.,Department of Medicine, Yong Loo Lin School of Medicine, National University Health System, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS, Medical School, Singapore
| | - Xuling Chang
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore
| | - Mark Yan-Yee Chan
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,National University Heart Centre, National University Health System, Singapore
| | - Wei Gao
- Department of Cardiology, Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China
| | - Hong Zheng
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Jing Chen
- Department of Medicine, Tulane University School of Medicine, and Tulane University Translational Science Institute, New Orleans, LA, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, and Tulane University Translational Science Institute, New Orleans, LA, USA
| | - Clara Sze-Man Tang
- Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Karen Siu Ling Lam
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Hung-Fat Tse
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Chloe Yu Yan Cheung
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Atsushi Takahashi
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Department of Genomic Medicine, Research Institute, National Cerebral and Cardiovascular Center, Osaka, Japan
| | - Michiaki Kubo
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Pak Chung Sham
- Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Chew-Kiat Heng
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Y Eugene Chen
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Tangchun Wu
- MOE Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan 430030, China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Cristen J Willer
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA.,Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Dongfeng Gu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
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Beck K, Vincent A, Cam H, Becker C, Gross S, Loretz N, Müller J, Amacher SA, Bohren C, Sutter R, Bassetti S, Hunziker S. Medical futility regarding cardiopulmonary resuscitation in in-hospital cardiac arrests of adult patients: A systematic review and Meta-analysis. Resuscitation 2021; 172:181-193. [PMID: 34896244 DOI: 10.1016/j.resuscitation.2021.11.041] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 11/29/2021] [Indexed: 11/19/2022]
Abstract
AIM For some patients, survival with good neurologic function after cardiopulmonary resuscitation (CPR) is highly unlikely, thus CPR would be considered medically futile. Yet, in clinical practice, there are no well-established criteria, guidelines or measures to determine futility. We aimed to investigate how medical futility for CPR in adult patients is defined, measured, and associated with do-not-resuscitate (DNR) code status as well as to evaluate the predictive value of clinical risk scores through meta-analysis. METHODS We searched Embase, PubMed, CINAHL, and PsycINFO from the inception of each database up to January 22, 2021. Data were pooled using a fixed-effects model. Data collection and reporting followed the PRISMA guidelines. RESULTS Thirty-one studies were included in the systematic review and 11 in the meta-analysis. Medical futility defined by risk scores was associated with a significantly higher risk of in-hospital mortality (5 studies, 3102 participants with Pre-Arrest Morbidity (PAM) and Prognosis After Resuscitation (PAR) score; overall RR 3.38 [95% CI 1.92-5.97]) and poor neurologic outcome/in-hospital mortality (6 studies, 115,213 participants with Good Outcome Following Attempted Resuscitation (GO-FAR) and Prediction of Outcome for In-Hospital Cardiac Arrest (PIHCA) score; RR 6.93 [95% CI 6.43-7.47]). All showed high specificity (>90%) for identifying patients with poor outcome. CONCLUSION There is no international consensus and a lack of specific definitions of CPR futility in adult patients. Clinical risk scores might aid decision-making when CPR is assumed to be futile. Future studies are needed to assess their clinical value and reliability as a measure of futility regarding CPR.
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Affiliation(s)
- Katharina Beck
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland
| | - Alessia Vincent
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland; Division of Clinical Psychology and Psychotherapy, Faculty of Psychology, University of Basel, Missionsstrasse 60/62, 4055 Basel, Switzerland
| | - Hasret Cam
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland
| | - Christoph Becker
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland; Department of Emergency Medicine, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Sebastian Gross
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland
| | - Nina Loretz
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland
| | - Jonas Müller
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland
| | - Simon A Amacher
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland; Clinic of Intensive Care, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Chantal Bohren
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland
| | - Raoul Sutter
- Clinic of Intensive Care, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Stefano Bassetti
- Medical Faculty, University of Basel, Klingelbergstrasse 61, 4031 Basel, Switzerland; Division of Internal Medicine, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Sabina Hunziker
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Klingelbergstrasse 23, 4031 Basel, Switzerland; Medical Faculty, University of Basel, Klingelbergstrasse 61, 4031 Basel, Switzerland.
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Zhai W, Duan F, Li D, Yan Q, Dai S, Zhang B, Wang J. Risk stratification and adjuvant chemotherapy after radical resection based on the clinical risk scores of patients with stage IB-IIA non-small cell lung cancer. Eur J Surg Oncol 2021; 48:752-760. [PMID: 34620508 DOI: 10.1016/j.ejso.2021.09.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 09/11/2021] [Accepted: 09/29/2021] [Indexed: 12/25/2022] Open
Abstract
INTRODUCTION Despite the heterogeneity among patients with stage IB-IIA non-small cell lung cancer (NSCLC), clinically applicable models to identify patients most suitable for receiving adjuvant chemotherapy (ACT) are limited. We aimed to develop a model for risk stratification and the individualized application of ACT. METHODS Between January 2008 and March 2018, patients with T2N0M0 NSCLC at Sun Yat-sen University Cancer Center were retrospectively enrolled. Survival curves were estimated by Kaplan-Meier method and compared with log-rank test. Cox regression models were used to identify prognostic factors for disease-free survival (DFS) and overall survival (OS). Propensity score matching (PSM) was implemented. Subgroup analysis was performed based on clinical risk score (CRS) value and epidermal growth factor receptor (EGFR) mutation status. RESULTS Of 1063 patients with T2N0 NSCLC enrolled, 272 patients received ACT. Before PSM, patients with high CRS (>1) had a significantly worse OS and DFS outcomes. In the PSM, the baseline characteristics of the 270 pairs of patients were well matched. ACT was associated with improved OS outcomes for patients with a high CRS, while ACT was associated with improved OS and DFS outcomes in patients with wild-type EGFR. The interaction analysis showed an apparent interaction effect between ACT and EGFR-activating mutations as well as chemotherapy regimens and histology. CONCLUSIONS The CRS can predict the prognosis of patients with stage IB-IIA NSCLC. ACT could improve the outcome of patients with a high CRS. Patients with non-squamous cell histology receiving pemetrexed plus platinum might benefit more, but not those with EGFR-activating mutations.
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Affiliation(s)
- Wenyu Zhai
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, PR China
| | - Fangfang Duan
- VIP Region, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, PR China
| | - Dongxia Li
- The Second Department of Surgery, Sun Yat-sen University Sixth Affiliated Hospital, Guangzhou, Guangdong, PR China
| | - Qihang Yan
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, PR China
| | - Shuqin Dai
- Department of Laboratory Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, PR China
| | - Bei Zhang
- VIP Region, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, PR China.
| | - Junye Wang
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, PR China.
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Chen EY, Mayo SC, Sutton T, Kearney MR, Kardosh A, Vaccaro GM, Billingsley KG, Lopez CD. Effect of Time to Surgery of Colorectal Liver Metastases on Survival. J Gastrointest Cancer 2021; 52:169-176. [PMID: 32086781 PMCID: PMC7900034 DOI: 10.1007/s12029-020-00372-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Purpose Resection of liver-only colorectal liver metastases (CRLM) with perioperative chemotherapy is potentially curative. Specific primary tumor and liver metastasis characteristics have been validated to estimate the risk of recurrence. We hypothesize that the time interval from diagnosis of CRLM to surgery, or time to surgery (TTS), is clinically prognostic. Methods Patients from a prospectively maintained institutional database at a Comprehensive Cancer Center from May 2003 to January 2018 were reviewed. Clinicopathologic, perioperative treatment, and TTS data were collected. TTS was categorized into short (< 3 months), intermediate (3–6 months), and long (> 6 months) intervals. Results Two hundred eighty-one patients were identified. While overall survival (OS) was similar across TTS, postoperative overall survival (postoperative OS) of long TTS was associated with worse survival, 44 months (95% CI, 34–52) compared to short TTS, 59 months (95% CI, 43–79), and intermediate TTS, 63 months (95% CI, 52–108), both p < 0.01. With regard to long-term OS, intermediate TTS had 5-year OS of 59% and 8-year OS of 43% compared to long TTS (5-year OS 53% and 8-year OS 18%) and short TTS (5-year OS 54% and 8-year OS 29%). Long TTS was negatively associated with postoperative OS on multivariate analysis (HR 1.6, p < 0.01) when adjusting for resection margin, CRLM size, age, and use of postoperative chemotherapy. Conclusion Short and intermediate TTS had similar survival although patients with intermediate TTS may have better odds of long-term OS. While long TTS was associated with worse survival, likely due to higher disease burden, long-term survivors were still observed. Electronic supplementary material The online version of this article (10.1007/s12029-020-00372-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Emerson Y Chen
- Division of Hematology Oncology, Knight Cancer Institute, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, OC14HO, Portland, OR, 97239, USA.
| | - Skye C Mayo
- Division of Surgical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Thomas Sutton
- Division of Surgical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Matthew R Kearney
- Division of Hematology Oncology, Knight Cancer Institute, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, OC14HO, Portland, OR, 97239, USA
| | - Adel Kardosh
- Division of Hematology Oncology, Knight Cancer Institute, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, OC14HO, Portland, OR, 97239, USA
| | - Gina M Vaccaro
- Division of Hematology Oncology, Knight Cancer Institute, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, OC14HO, Portland, OR, 97239, USA
| | - Kevin G Billingsley
- Division of Surgical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Charles D Lopez
- Division of Hematology Oncology, Knight Cancer Institute, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, OC14HO, Portland, OR, 97239, USA.
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Hoogeveen RM, Pereira JPB, Nurmohamed NS, Zampoleri V, Bom MJ, Baragetti A, Boekholdt SM, Knaapen P, Khaw KT, Wareham NJ, Groen AK, Catapano AL, Koenig W, Levin E, Stroes ESG. Improved cardiovascular risk prediction using targeted plasma proteomics in primary prevention. Eur Heart J 2020; 41:3998-4007. [PMID: 32808014 PMCID: PMC7672529 DOI: 10.1093/eurheartj/ehaa648] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 02/13/2020] [Accepted: 07/27/2020] [Indexed: 01/04/2023] Open
Abstract
AIMS In the era of personalized medicine, it is of utmost importance to be able to identify subjects at the highest cardiovascular (CV) risk. To date, single biomarkers have failed to markedly improve the estimation of CV risk. Using novel technology, simultaneous assessment of large numbers of biomarkers may hold promise to improve prediction. In the present study, we compared a protein-based risk model with a model using traditional risk factors in predicting CV events in the primary prevention setting of the European Prospective Investigation (EPIC)-Norfolk study, followed by validation in the Progressione della Lesione Intimale Carotidea (PLIC) cohort. METHODS AND RESULTS Using the proximity extension assay, 368 proteins were measured in a nested case-control sample of 822 individuals from the EPIC-Norfolk prospective cohort study and 702 individuals from the PLIC cohort. Using tree-based ensemble and boosting methods, we constructed a protein-based prediction model, an optimized clinical risk model, and a model combining both. In the derivation cohort (EPIC-Norfolk), we defined a panel of 50 proteins, which outperformed the clinical risk model in the prediction of myocardial infarction [area under the curve (AUC) 0.754 vs. 0.730; P < 0.001] during a median follow-up of 20 years. The clinically more relevant prediction of events occurring within 3 years showed an AUC of 0.732 using the clinical risk model and an AUC of 0.803 for the protein model (P < 0.001). The predictive value of the protein panel was confirmed to be superior to the clinical risk model in the validation cohort (AUC 0.705 vs. 0.609; P < 0.001). CONCLUSION In a primary prevention setting, a proteome-based model outperforms a model comprising clinical risk factors in predicting the risk of CV events. Validation in a large prospective primary prevention cohort is required to address the value for future clinical implementation in CV prevention.
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Affiliation(s)
- Renate M Hoogeveen
- Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - João P Belo Pereira
- Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Nick S Nurmohamed
- Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Veronica Zampoleri
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Via Balzaretti 9, 20133 Milan, Italy
| | - Michiel J Bom
- Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Andrea Baragetti
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Via Balzaretti 9, 20133 Milan, Italy
| | - S Matthijs Boekholdt
- Department of Cardiology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Paul Knaapen
- Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge, 2 Worts' Causeway, Cambridge, UK
| | - Nicholas J Wareham
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Albert K Groen
- Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Alberico L Catapano
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Via Balzaretti 9, 20133 Milan, Italy
- Multimedica IRCCS, Milano, Italy
| | - Wolfgang Koenig
- Klinik für Herz- und Kreislauferkrankungen, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner site Munich Heart Alliance, Munich, Germany
- Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany
| | - Evgeni Levin
- Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- HorAIzon BV, Delft, the Netherlands
| | - Erik S G Stroes
- Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
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Abstract
Tricuspid valve (TV) surgery carries high mortality and morbidity, and risk-adjusted mortality has not changed. Patients who can withstand the perioperative period benefit from symptomatic improvement as the right ventricle remodels. Risk stratification for patients undergoing surgical intervention is critically important. The Model for End-Stage Liver Disease (MELD) score is a reliable and accurate mortality risk predictor given the liver and kidney dysfunction that accompany tricuspid regurgitation. Novel clinical risk calculators for isolated TV surgery have also been developed to further guide patients with projected surgical outcomes and reinforce timeliness to intervention.
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Affiliation(s)
- Evan Rotar
- Division of Cardiac Surgery, Department of Surgery, University of Virginia, Charlottesville, VA, United States of America
| | - D Scott Lim
- Division of Cardiology, Department of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Gorav Ailawadi
- Division of Cardiac Surgery, Department of Surgery, University of Virginia, Charlottesville, VA, United States of America.
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Ayez N, van der Stok EP, Grünhagen DJ, Rothbarth J, van Meerten E, Eggermont AM, Verhoef C. The use of neo-adjuvant chemotherapy in patients with resectable colorectal liver metastases: Clinical risk score as possible discriminator. Eur J Surg Oncol 2015; 41:859-67. [PMID: 25979624 DOI: 10.1016/j.ejso.2015.04.012] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2014] [Revised: 03/18/2015] [Accepted: 04/16/2015] [Indexed: 12/21/2022] Open
Abstract
AIM The combination of surgery and chemotherapy (CTx) is increasingly accepted as an effective treatment for patients with colorectal liver metastases (CRLM). However, controversy exists whether all patients with resectable CRLM benefit from perioperative CTx. We investigated the impact on overall survival (OS) by neo-adjuvant CTx in patients with resectable CRLM, stratified by the clinical risk score (CRS) described by Fong et al. METHODS Patients who underwent surgery for CRLM between January 2000 and December 2009 were included. We compared OS of patients with and without neo-adjuvant CTx stratified by the CRS. The CRS includes five prognosticators and defines two risk groups: low CRS (0-2) and high CRS (3-5). RESULTS 363 patients (64% male) were included, median age 63 years (IQR 57-70). Prior to resection, 219 patients had a low CRS (neo-adjuvant CTx: N = 65) and 144 patients had a high CRS (neo-adjuvant CTx: N = 88). Median follow-up was 47 months (IQR 25-82). In the low CRS group, there was no significant difference in median OS between patients with and without CTx (65 months (95% CI 39-91) vs. 54 months (95% CI 44-64), P = 0.31). In the high CRS group, there was a significant difference in OS between patients with and without CTx (46 months (95% CI 24-68) vs. 33 month (95% CI 29-37), P = 0.004). CONCLUSION In our series, patients with a high CRS benefit from neo-adjuvant CTx. In patients with a low risk profile, neo-adjuvant CTx might not be beneficial.
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Affiliation(s)
- N Ayez
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Erasmus University, Rotterdam, The Netherlands
| | - E P van der Stok
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Erasmus University, Rotterdam, The Netherlands
| | - D J Grünhagen
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Erasmus University, Rotterdam, The Netherlands.
| | - J Rothbarth
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Erasmus University, Rotterdam, The Netherlands
| | - E van Meerten
- Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus University, Rotterdam, The Netherlands
| | - A M Eggermont
- Cancer Institute, Gustave Roussy Cancer Campus, Grand Paris, France
| | - C Verhoef
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Erasmus University, Rotterdam, The Netherlands
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Oliphant R, Horgan PG, Morrison DS, McMillan DC. Validation of a modified clinical risk score to predict cancer-specific survival for stage II colon cancer. Cancer Med 2014; 4:84-9. [PMID: 25487740 PMCID: PMC4312121 DOI: 10.1002/cam4.352] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2014] [Revised: 08/12/2014] [Accepted: 08/20/2014] [Indexed: 01/20/2023] Open
Abstract
Many patients with stage II colon cancer will die of their disease despite curative surgery. Therefore, identification of patients at high risk of poor outcome after surgery for stage II colon cancer is desirable. This study aims to validate a clinical risk score to predict cancer-specific survival in patients undergoing surgery for stage II colon cancer. Patients undergoing surgery for stage II colon cancer in 16 hospitals in the West of Scotland between 2001 and 2004 were identified from a prospectively maintained regional clinical audit database. Overall and cancer-specific survival rates up to 5 years were calculated. A total of 871 patients were included. At 5 years, cancer-specific survival was 81.9% and overall survival was 65.6%. On multivariate analysis, age ≥75 years (hazard ratio (HR) 2.11, 95% confidence intervals (CI) 1.57–2.85; P<0.001) and emergency presentation (HR 1.97, 95% CI 1.43–2.70; P<0.001) were independently associated with cancer-specific survival. Age and mode of presentation HRs were added to form a clinical risk score of 0–2. The cancer-specific survival at 5 years for patients with a cumulative score 0 was 88.7%, 1 was 78.2% and 2 was 65.9%. These results validate a modified simple clinical risk score for patients undergoing surgery for stage II colon cancer. The combination of these two universally documented clinical factors provides a solid foundation for the examination of the impact of additional clinicopathological and treatment factors on overall and cancer-specific survival.
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Affiliation(s)
- Raymond Oliphant
- University Department of Surgery, Faculty of Medicine, University of Glasgow, Glasgow Royal Infirmary, Glasgow, G4 0SF, U.K; West of Scotland Cancer Surveillance Unit, Institute of Health and Wellbeing, University of Glasgow, Glasgow, G12 8RZ, U.K
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