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Zhu F, Wang P, Zhang Z, Yao C, Wang Y, Ye J, Wu J. Integrative genomic analysis reveals cancer-associated mutations in patients with ophthalmic tumors. J Int Med Res 2024; 52:3000605241258171. [PMID: 39053449 PMCID: PMC11283671 DOI: 10.1177/03000605241258171] [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: 09/12/2023] [Accepted: 05/04/2024] [Indexed: 07/27/2024] Open
Abstract
OBJECTIVE Apart from the role of the retinoblastoma gene, the genomic events associated with poor outcomes in patients with ophthalmic tumors are poorly understood. METHODS We retrospectively analyzed 48 patients with six types of ophthalmic tumors. We searched for high-frequency mutated genes and susceptibility genes in these patients using combined exome and transcriptome analysis. RESULTS We identified four clearly causative genes (TP53, PTCH1, SMO, BAP1). Susceptibility gene analysis identified hotspot genes, including RUNX1, APC, IDH2, and BRCA2, and high-frequency gene analysis identified several genes, including TP53, TTN, and MUC16. Transcriptome analysis identified 5868 differentially expressed genes, of which TOP2A and ZWINT were upregulated in all samples, while CFD, ELANE, HBA1, and HBB were downregulated. Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that the phosphoinositide 3-kinase (PI3K)-Akt and Transcriptional misregulation in cancer signaling pathways may be involved in ophthalmic tumorigenesis. CONCLUSIONS TP53 is clearly involved in ophthalmic tumorigenesis, especially in basal cell carcinoma, and the PI3K-Akt signaling pathway may be an essential pathway involved in ophthalmic tumorigenesis. RUNX1, SMO, TOP2A, and ZWINT are also highly likely to be involved in ophthalmic tumorigenesis, but further functional experiments are needed to verify the mechanisms of these genes in regulating tumorigenesis.
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Affiliation(s)
| | | | | | - Chunlei Yao
- Department of Ophthalmology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yijie Wang
- Department of Ophthalmology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Juan Ye
- Department of Ophthalmology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jian Wu
- MyGenostics Inc., Beijing, China
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2
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Kriukova KK, Alexandrova EV, Voskresenskaya ON, Podlepich VV, Kravchuk AD, Rytkin EI, Latyshev YA, Kudlay DA, Sologova SS, Albagachiev SA, Mandrik MA. [Predictive capability of Cys112Arg single nucleotide polymorphisms of the apolipoprotein E gene in assessing the risk of immediate and early post-traumatic seizures]. TERAPEVT ARKH 2023; 95:1128-1132. [PMID: 38785052 DOI: 10.26442/00403660.2023.12.202492] [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] [Received: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 05/25/2024]
Abstract
This study is aimed at investigating epileptic seizures, one of the consequences of traumatic brain injury (TBI). Immediate and early post-traumatic seizures, as well as late post-traumatic epileptic seizures or post-traumatic epilepsy, can have different pathogenetic bases. The following key risk factors associated with post-traumatic epilepsy are known: duration of unconsciousness, gunshot wounds, intracranial hemorrhage, diffuse axonal injury, prolonged (more than 3 days) post-traumatic amnesia, acute subdural hematoma with surgical evacuation, immediate and early post-traumatic epileptic seizures, fracture of the skull bones. The role of genetic factors in post-traumatic seizures is poorly understood due to the complexity and multiple causal mechanisms. This paper addresses the role of genetic factors in the occurrence and severity of epileptic events in patients with TBI. In particular, we investigated the role of the Cys112Arg single nucleotide polymorphism of the apolipoprotein E gene. Apolipoprotein E is known for its role in the transport and metabolism of lipids and, therefore, the development of cardiovascular diseases; it is also associated with Alzheimer's disease and has recently been studied in the context of association with epilepsy. The study shows an association between this polymorphism and the risk of immediate and early epileptic seizures in patients with severe TBI.
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Affiliation(s)
- K K Kriukova
- Sechenov First Moscow State Medical University (Sechenov University)
| | | | | | - V V Podlepich
- Burdenko National Medical Research Center for Neurosurgery
| | - A D Kravchuk
- Burdenko National Medical Research Center for Neurosurgery
| | | | - Y A Latyshev
- Burdenko National Medical Research Center for Neurosurgery
| | - D A Kudlay
- Sechenov First Moscow State Medical University (Sechenov University)
| | - S S Sologova
- Sechenov First Moscow State Medical University (Sechenov University)
| | - S A Albagachiev
- Sechenov First Moscow State Medical University (Sechenov University)
| | - M A Mandrik
- Sechenov First Moscow State Medical University (Sechenov University)
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3
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Castro Gonçalves AB, Ferreira Fratelli C, Saraiva Siqueira JW, Canongia de Abreu Cardoso Duarte L, Ribeiro Barros A, Possatti I, Lima dos Santos M, de Souza Silva CM, Rodrigues da Silva IC. MAOA uVNTR Genetic Variant and Major Depressive Disorder: A Systematic Review. Cells 2022; 11:cells11203267. [PMID: 36291132 PMCID: PMC9600429 DOI: 10.3390/cells11203267] [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: 07/11/2022] [Revised: 09/12/2022] [Accepted: 09/16/2022] [Indexed: 12/04/2022] Open
Abstract
Major Depressive Disorder (MDD) is a highly prevalent multifactorial psychopathology affected by neurotransmitter levels. Monoamine Oxidase A (MAOA) influences several neural pathways by modulating these levels. This systematic review (per PRISMA protocol and PECOS strategy) endeavors to understand the MAOA uVNTR polymorphism influence on MDD and evaluate its 3R/3R and 3R* genotypic frequencies fluctuation in MDD patients from different populations. We searched the Web of Science, PubMed, Virtual Health Library, and EMBASE databases for eligible original articles that brought data on genotypic frequencies related to the MAOA uVNTR variant in patients with MDD. We excluded studies with incomplete data (including statistical data), reviews, meta-analyses, and abstracts. Initially, we found 43 articles. After removing duplicates and applying the inclusion/exclusion criteria, seven articles remained. The population samples studied were predominantly Asians, with high 3R and 4R allele frequencies. Notably, we observed higher 3R/3R (female) and 3R* (male) genotype frequencies in the healthy control groups and higher 4R/4R (female) and 4R* (male) genotype frequencies in the MDD groups in the majority of different populations. Despite some similarities in the articles analyzed, there is still no consensus on the MAOA uVNTR variant’s role in MDD pathogenesis.
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Affiliation(s)
- Ana Beatriz Castro Gonçalves
- Pharmacy Course, Faculty of Ceilândia, University of Brasília (UnB), Brasília—Federal District (DF), Brasília 72220-900, Brazil
| | - Caroline Ferreira Fratelli
- Postgraduate Program in Health Sciences and Technologies, Faculty of Ceilândia, University of Brasília (UnB), Brasília—Federal District (DF), Brasília 72220-900, Brazil
| | - Jhon Willatan Saraiva Siqueira
- Pharmacy Course, Faculty of Ceilândia, University of Brasília (UnB), Brasília—Federal District (DF), Brasília 72220-900, Brazil
| | - Ligia Canongia de Abreu Cardoso Duarte
- Postgraduate Program in Health Sciences and Technologies, Faculty of Ceilândia, University of Brasília (UnB), Brasília—Federal District (DF), Brasília 72220-900, Brazil
| | - Aline Ribeiro Barros
- Postgraduate Program in Health Sciences and Technologies, Faculty of Ceilândia, University of Brasília (UnB), Brasília—Federal District (DF), Brasília 72220-900, Brazil
| | - Isabella Possatti
- Postgraduate Program in Health Sciences and Technologies, Faculty of Ceilândia, University of Brasília (UnB), Brasília—Federal District (DF), Brasília 72220-900, Brazil
| | - Maurício Lima dos Santos
- Clinical Analysis Laboratory, Molecular Pathology Sector, Pharmacy Department, Faculty of Ceilândia, University of Brasília (UnB), Brasília—Federal District (DF), Brasília 72220-900, Brazil
| | - Calliandra Maria de Souza Silva
- Clinical Analysis Laboratory, Molecular Pathology Sector, Pharmacy Department, Faculty of Ceilândia, University of Brasília (UnB), Brasília—Federal District (DF), Brasília 72220-900, Brazil
| | - Izabel Cristina Rodrigues da Silva
- Clinical Analysis Laboratory, Molecular Pathology Sector, Pharmacy Department, Faculty of Ceilândia, University of Brasília (UnB), Brasília—Federal District (DF), Brasília 72220-900, Brazil
- Correspondence: ; Tel.: +55-(61)-3107-8400
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4
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Guijosa A, Freyria A, Espinosa‐Fernandez JR, Estrada‐Mena FJ, Armenta‐Quiroga AS, Ortega‐Treviño MF, Catalán R, Antonio‐Aguirre B, Villarreal‐Garza C, Perez‐Ortiz AC. Pharmacogenetics of taxane-induced neurotoxicity in breast cancer: Systematic review and meta-analysis. Clin Transl Sci 2022; 15:2403-2436. [PMID: 35892315 PMCID: PMC9579387 DOI: 10.1111/cts.13370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/09/2022] [Accepted: 06/20/2022] [Indexed: 01/25/2023] Open
Abstract
Taxane-based chemotherapy regimens are used as first-line treatment for breast cancer. Neurotoxicity, mainly taxane-induced peripheral neuropathy (TIPN), remains the most important dose-limiting adverse event. Multiple genes may be associated with TIPN; however, the strength and direction of the association remain unclear. For this reason, we systematically reviewed observational studies of TIPN pharmacogenetic markers in breast cancer treatment. We conducted a systematic search of terms alluding to breast cancer, genetic markers, taxanes, and neurotoxicity in Ovid, ProQuest, PubMed, Scopus, Virtual Health, and Web of Science. We assessed the quality of evidence and bias profile. We extracted relevant variables and effect measures. Whenever possible, we performed random-effects gene meta-analyses and examined interstudy heterogeneity with meta-regression models and subgroup analyses. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and STrengthening the REporting of Genetic Association Studies (STREGA) reporting guidance. A total of 42 studies with 19,431 participants were included. These evaluated 262 single-nucleotide polymorphisms (SNPs) across 121 genes. We conducted meta-analyses on 23 genes with 60 SNPs (19 studies and 6246 participants). Thirteen individual SNPs (ABCB1-rs2032582, ABCB1-rs3213619, BCL6/-rs1903216, /CAND1-rs17781082, CYP1B1-rs1056836, CYP2C8-rs10509681, CYP2C8-rs11572080, EPHA5-rs7349683, EPHA6-rs301927, FZD3-rs7001034, GSTP1-rs1138272, TUBB2A-rs9501929, and XKR4-rs4737264) and the overall SNPs' effect in four genes (CYP3A4, EphA5, GSTP1, and SLCO1B1) were statistically significantly associated with TIPN through meta-analysis. In conclusion, through systematic review and meta-analysis, we found that polymorphisms, and particularly 13 SNPs, are associated with TIPN, suggesting that genetics does play a role in interindividual predisposition. Further studies could potentially use these findings to develop individual risk profiles and guide decision making.
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Affiliation(s)
| | - Ana Freyria
- School of MedicineUniversidad PanamericanaMexico CityMexico
| | | | | | | | | | - Rodrigo Catalán
- School of MedicineUniversidad PanamericanaMexico CityMexico,Thoracic Oncology UnitInstituto Nacional de CancerologíaMexico CityMexico
| | | | - Cynthia Villarreal‐Garza
- Breast Cancer Center, Hospital Zambrano Hellion TecSalud, Tecnologico de MonterreySan Pedro Garza GarcíaNuevo LeónMexico
| | - Andric C. Perez‐Ortiz
- School of MedicineUniversidad PanamericanaMexico CityMexico,Transplant CenterMassachusetts General HospitalBostonMassachusettsUSA
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5
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Khan SS, Page C, Wojdyla DM, Schwartz YY, Greenland P, Pencina MJ. Predictive Utility of a Validated Polygenic Risk Score for Long-Term Risk of Coronary Heart Disease in Young and Middle-Aged Adults. Circulation 2022; 146:587-596. [PMID: 35880530 PMCID: PMC9398962 DOI: 10.1161/circulationaha.121.058426] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
BACKGROUND Understanding the predictive utility of previously derived polygenic risk scores (PRSs) for long-term risk of coronary heart disease (CHD) and its additive value beyond traditional risk factors can inform prevention strategies. METHODS Data from adults 20 to 59 years of age who were free of CHD from the FOS (Framingham Offspring Study) and the ARIC (Atherosclerosis Risk in Communities) study were analyzed. Because the PRS was derived from samples of predominantly European ancestry, individuals who self-reported White race were included. The sample was stratified by age and cohort: young (FOS, 20-39 years [median, 30 years] of age), early midlife (FOS, 40-59 years [median, 43] years of age), and late midlife (ARIC, 45-59 years [median, 52 years] of age). Two previously derived and validated prediction tools were applied: (1) a 30-year traditional risk factor score and (2) a genome-wide PRS comprising >6 million genetic variants. Hazard ratios for the association between each risk estimate and incident CHD were calculated. Predicted and observed rates of CHD were compared to assess discrimination for each model individually and together with the optimism-corrected C index (95% CI). RESULTS Among 9757 participants, both the traditional risk factor score (hazard ratio per 1 SD, 2.60 [95% CI, 2.08-3.27], 2.09 [95% CI, 1.83-2.40], and 2.11 [95% CI, 1.96-2.28]) and the PRS (hazard ratio, 1.98 [95% CI, 1.70-2.30], 1.64 [95% CI, 1.47-1.84], and 1.22 [95% CI, 1.15-1.30]) were significantly associated with incident CHD in young, early midlife, and late midlife, respectively. Discrimination was similar or better for the traditional risk factor score (C index, 0.74 [95% CI, 0.70-0.78], 0.70 [95% CI, 0.67-0.72], and 0.72 [95% CI, 0.70-0.73]) compared with an age- and sex-adjusted PRS (0.73 [95% CI, 0.69-0.78], 0.66 [95% CI, 0.62-0.69], and 0.66 [95% CI, 0.64-0.67]) in young, early-midlife, and late-midlife participants, respectively. The ΔC index when PRS was added to the traditional risk factor score was 0.03 (95% CI, 0.001-0.05), 0.02 (95% CI, -0.002 to 0.037), and 0.002 (95% CI, -0.002 to 0.006) in young, early-midlife, and late-midlife participants, respectively. CONCLUSIONS Despite a statistically significant association between PRS and 30-year risk of CHD, the C statistic improved only marginally with the addition of PRS to the traditional risk factor model among young adults and did not improve among midlife adults. PRS, an immutable factor that cannot be directly intervened on, has minimal clinical utility for long-term CHD prediction when added to a traditional risk factor model.
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Affiliation(s)
- Sadiya S. Khan
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Courtney Page
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
| | - Daniel M. Wojdyla
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
| | - Yosef Y. Schwartz
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Philip Greenland
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Michael J. Pencina
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
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6
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Role of Polygenic Risk Score in Cancer Precision Medicine of Non-European Populations: A Systematic Review. Curr Oncol 2022; 29:5517-5530. [PMID: 36005174 PMCID: PMC9406904 DOI: 10.3390/curroncol29080436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/28/2022] [Accepted: 08/01/2022] [Indexed: 11/17/2022] Open
Abstract
The development of new screening methods and diagnostic tests for traits, common diseases, and cancer is linked to the advent of precision genomic medicine, in which health care is individually adjusted based on a person’s lifestyle, environmental influences, and genetic variants. Based on genome-wide association study (GWAS) analysis, rapid and continuing progress in the discovery of relevant single nucleotide polymorphisms (SNPs) for traits or complex diseases has increased interest in the potential application of genetic risk models for routine health practice. The polygenic risk score (PRS) estimates an individual’s genetic risk of a trait or disease, calculated by employing a weighted sum of allele counts combined with non-genetic variables. However, 98.38% of PRS records held in public databases relate to the European population. Therefore, PRSs for multiethnic populations are urgently needed. We performed a systematic review to discuss the role of polygenic risk scores in advancing precision medicine for different cancer types in multiethnic non-European populations.
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7
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BDNF Genetic Variant and Its Genotypic Fluctuation in Major Depressive Disorder. Behav Neurol 2021; 2021:7117613. [PMID: 34760029 PMCID: PMC8575598 DOI: 10.1155/2021/7117613] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 08/28/2021] [Accepted: 10/06/2021] [Indexed: 01/04/2023] Open
Abstract
Major depressive disorder (MDD) still has an unknown etiology and mechanisms. Many studies have been conducted seeking to associate and understand the connection of different genetic variants to this disease. Researchers have extensively studied the brain-derived neurotrophic factor (BDNF) Val66Met genetic variant in MDD; yet, their findings remain inconsistent. This systematic review sought to verify the GG (Val/Val) genotype frequency fluctuation in different populations with MDD. For this, we searched in different databases and, after applying the eligibility criteria, selected 17 articles. Most studies demonstrate the higher frequency of the ancestral (wild) GG (Val/Val) genotype, although associations of the polymorphic A (Met) allele, changes in BDNF protein serum levels, or both were also found in MDD, whether related to the disease's development or other factors. Nevertheless, despite these findings, disagreements between several studies are seen. For this reason, further BDNF Val66Met genetic variant studies should not only bridge the gap in the knowledge of this polymorphism's role in MDD's different facets but also analyze the genotypic and phenotypic heterogeneity in different populations to help provide a better quality of life for patients.
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8
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Fisch GS. Associating complex traits with genetic variants: polygenic risk scores, pleiotropy and endophenotypes. Genetica 2021; 150:183-197. [PMID: 34677750 DOI: 10.1007/s10709-021-00138-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/07/2021] [Indexed: 11/29/2022]
Abstract
Genotype-phenotype causal modeling has evolved significantly since Johannsen's and Wright's original designs were published. The development of genomewide assays to interrogate and detect possible causal variants associated with complex traits has expanded the scope of genotype-phenotype research considerably. Clusters of causal variants discovered by genomewide assays and associated with complex traits have been used to develop polygenic risk scores to predict clinical diagnoses of multidimensional human disorders. However, genomewide investigations have met with many challenges to their research designs and statistical complexities which have hindered the reliability and validity of their predictions. Findings linked to differences in heritability estimates between causal clusters and complex traits among unrelated individuals remain a research area of some controversy. Causal models developed from case-control studies as opposed to experiments, as well as other issues concerning the genotype-phenotype causal model and the extent to which various forms of pleiotropy and the concept of the endophenotype add to its complexity, will be reviewed.
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Affiliation(s)
- Gene S Fisch
- Paul H. Chook Dept. of CIS & Statistics, CUNY/Baruch College, New York, NY, USA.
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9
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Cherak SJ, Soo A, Brown KN, Ely EW, Stelfox HT, Fiest KM. Development and validation of delirium prediction model for critically ill adults parameterized to ICU admission acuity. PLoS One 2020; 15:e0237639. [PMID: 32813717 PMCID: PMC7437909 DOI: 10.1371/journal.pone.0237639] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 07/29/2020] [Indexed: 12/23/2022] Open
Abstract
Background Risk prediction models allow clinicians to forecast which individuals are at a higher risk for developing a particular outcome. We developed and internally validated a delirium prediction model for incident delirium parameterized to patient ICU admission acuity. Methods This retrospective, observational, fourteen medical-surgical ICU cohort study evaluated consecutive delirium-free adults surviving hospital stay with ICU length of stay (LOS) greater than or equal to 24 hours with both an admission APACHE II score and an admission type (e.g., elective post-surgery, emergency post-surgery, non-surgical) in whom delirium was assessed using the Intensive Care Delirium Screening Checklist (ICDSC). Risk factors included in the model were readily available in electric medical records. Least absolute shrinkage and selection operator logistic (LASSO) regression was used for model development. Discrimination was determined using area under the receiver operating characteristic curve (AUC). Internal validation was performed by cross-validation. Predictive performance was determined using measures of accuracy and clinical utility was assessed by decision-curve analysis. Results A total of 8,878 patients were included. Delirium incidence was 49.9% (n = 4,431). The delirium prediction model was parameterized to seven patient cohorts, admission type (3 cohorts) or mean quartile APACHE II score (4 cohorts). All parameterized cohort models were well calibrated. The AUC ranged from 0.67 to 0.78 (95% confidence intervals [CI] ranged from 0.63 to 0.79). Model accuracy varied across admission types; sensitivity ranged from 53.2% to 63.9% while specificity ranged from 69.0% to 74.6%. Across mean quartile APACHE II scores, sensitivity ranged from 58.2% to 59.7% while specificity ranged from 70.1% to 73.6%. The clinical utility of the parameterized cohort prediction model to predict and prevent incident delirium was greater than preventing incident delirium by treating all or none of the patients. Conclusions Our results support external validation of a prediction model parameterized to patient ICU admission acuity to predict a patients’ risk for ICU delirium. Classification of patients’ risk for ICU delirium by admission acuity may allow for efficient initiation of prevention measures based on individual risk profiles.
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Affiliation(s)
- Stephana J. Cherak
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- O’Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Andrea Soo
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Health Services, Calgary, AB, Canada
| | - Kyla N. Brown
- PolicyWise for Children & Families, Calgary, AB, Canada
| | - E. Wesley Ely
- Tennessee Valley Veteran’s Affairs Geriatric Research Education Clinical Center, Nashville, TN, United States of America
- Critical Illness, Brain Dysfunction, and Survivorship Center, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Henry T. Stelfox
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- O’Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada
- Alberta Health Services, Calgary, AB, Canada
| | - Kirsten M. Fiest
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- O’Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Alberta Health Services, Calgary, AB, Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary AB, Canada
- * E-mail:
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10
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Fung SM, Wong XY, Lee SX, Miao H, Hartman M, Wee HL. Performance of Single-Nucleotide Polymorphisms in Breast Cancer Risk Prediction Models: A Systematic Review and Meta-analysis. Cancer Epidemiol Biomarkers Prev 2018; 28:506-521. [DOI: 10.1158/1055-9965.epi-18-0810] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 10/30/2018] [Accepted: 12/03/2018] [Indexed: 11/16/2022] Open
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11
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Ioannidis JPA, Bossuyt PMM. Waste, Leaks, and Failures in the Biomarker Pipeline. Clin Chem 2017; 63:963-972. [DOI: 10.1373/clinchem.2016.254649] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 11/30/2016] [Indexed: 01/05/2023]
Abstract
Abstract
BACKGROUND
The large, expanding literature on biomarkers is characterized by almost ubiquitous significant results, with claims about the potential importance, but few of these discovered biomarkers are used in routine clinical care.
CONTENT
The pipeline of biomarker development includes several specific stages: discovery, validation, clinical translation, evaluation, implementation (and, in the case of nonutility, deimplementation). Each of these stages can be plagued by problems that cause failures of the overall pipeline. Some problems are nonspecific challenges for all biomedical investigation, while others are specific to the peculiarities of biomarker research. Discovery suffers from poor methods and incomplete and selective reporting. External independent validation is limited. Selection for clinical translation is often shaped by nonrational choices. Evaluation is sparse and the clinical utility of many biomarkers remains unknown. The regulatory environment for biomarkers remains weak and guidelines can reach biased or divergent recommendations. Removing inefficient or even harmful biomarkers that have been entrenched in clinical care can meet with major resistance.
SUMMARY
The current biomarker pipeline is too prone to failures. Consideration of clinical needs should become a starting point for the development of biomarkers. Improvements can include the use of more stringent methodology, better reporting, larger collaborative studies, careful external independent validation, preregistration, rigorous systematic reviews and umbrella reviews, pivotal randomized trials, and implementation and deimplementation studies. Incentives should be aligned toward delivering useful biomarkers.
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Affiliation(s)
- John P A Ioannidis
- Departments of Medicine, Health Research and Policy, and Statistics, and the Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA
| | - Patrick M M Bossuyt
- Department of Clinical Epidemiology, Biostatistics & Bioinformatics, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
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12
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van Hecke O, Kamerman PR, Attal N, Baron R, Bjornsdottir G, Bennett DL, Bennett MI, Bouhassira D, Diatchenko L, Freeman R, Freynhagen R, Haanpää M, Jensen TS, Raja SN, Rice AS, Seltzer Z, Thorgeirsson TE, Yarnitsky D, Smith BH. Neuropathic pain phenotyping by international consensus (NeuroPPIC) for genetic studies: a NeuPSIG systematic review, Delphi survey, and expert panel recommendations. Pain 2015; 156:2337-2353. [PMID: 26469320 PMCID: PMC4747983 DOI: 10.1097/j.pain.0000000000000335] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Revised: 07/31/2015] [Accepted: 08/12/2015] [Indexed: 01/09/2023]
Abstract
For genetic research to contribute more fully to furthering our knowledge of neuropathic pain, we require an agreed, valid, and feasible approach to phenotyping, to allow collaboration and replication in samples of sufficient size. Results from genetic studies on neuropathic pain have been inconsistent and have met with replication difficulties, in part because of differences in phenotypes used for case ascertainment. Because there is no consensus on the nature of these phenotypes, nor on the methods of collecting them, this study aimed to provide guidelines on collecting and reporting phenotypes in cases and controls for genetic studies. Consensus was achieved through a staged approach: (1) systematic literature review to identify all neuropathic pain phenotypes used in previous genetic studies; (2) Delphi survey to identify the most useful neuropathic pain phenotypes and their validity and feasibility; and (3) meeting of experts to reach consensus on the optimal phenotype(s) to be collected from patients with neuropathic pain for genetic studies. A basic "entry level" set of phenotypes was identified for any genetic study of neuropathic pain. This set identifies cases of "possible" neuropathic pain, and controls, and includes: (1) a validated symptom-based questionnaire to determine whether any pain is likely to be neuropathic; (2) body chart or checklist to identify whether the area of pain distribution is neuroanatomically logical; and (3) details of pain history (intensity, duration, any formal diagnosis). This NeuroPPIC "entry level" set of phenotypes can be expanded by more extensive and specific measures, as determined by scientific requirements and resource availability.
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Affiliation(s)
- Oliver van Hecke
- Division of Population Health Sciences, University of Dundee, Dundee, Scotland, United Kingdom
| | - Peter R. Kamerman
- Brain Function Research Group, School of Physiology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Nadine Attal
- INSERM U-987, Centre d'Evaluation et de Traitement de la Douleur, CHU Ambroise Paré, Assistance Publique Hôpitaux de Paris, Boulogne-Billancourt, France
- Université Versailles Saint-Quentin, Versailles, France
| | - Ralf Baron
- Division of Neurological Pain Research and Therapy, Department of Neurology, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | | | - David L.H. Bennett
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Michael I. Bennett
- Academic Unit of Palliative Care, Leeds Institute of Health Sciences, University of Leeds, Leeds, United Kingdom
| | - Didier Bouhassira
- INSERM U-987, Centre d'Evaluation et de Traitement de la Douleur, CHU Ambroise Paré, Assistance Publique Hôpitaux de Paris, Boulogne-Billancourt, France
- Université Versailles Saint-Quentin, Versailles, France
| | - Luda Diatchenko
- The Alan Edwards Centre for Research on Pain, McGill University, Montreal, Canada
| | - Roy Freeman
- Department of Neurology, Beth Israel Deaconess Medical Centre, Harvard Medical School, Boston, USA
| | - Rainer Freynhagen
- Zentrum für Anästhesiologie, Intensivmedizin, Schmerztherapie und Palliativmedizin, Benedictus Krankenhaus, Tutzing, Germany
- Klinik für Anästhesiologie, Technische Universität München, München, Germany
| | - Maija Haanpää
- Department of Neurosurgery, Helsinki University Central Hospital, Helsinki, Finland
- Mutual Insurance Company Etera, Helsinki, Finland
| | - Troels S. Jensen
- Department of Clinical Medicine, Danish Pain Research Center, Aarhus University, Aarhus, Denmark
- Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
| | - Srinivasa N. Raja
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, School of Medicine, Baltimore, USA
| | - Andrew S.C. Rice
- Pain Research, Department of Surgery and Cancer, Faculty of Medicine, Imperial College, Chelsea and Westminster Hospital campus, London, United Kingdom
| | - Ze'ev Seltzer
- Pain Phenomics and Genomics Lab, University of Toronto Centre for the Study of Pain, University of Toronto, Toronto, Canada
| | | | - David Yarnitsky
- Department of Neurology, Technion Faculty of Medicine, Rambam Health Care Campus, Haifa, Israel (O. van Hecke is now with Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom)
| | - Blair H. Smith
- Division of Population Health Sciences, University of Dundee, Dundee, Scotland, United Kingdom
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Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement. Eur Urol 2015; 67:1142-1151. [PMID: 25572824 DOI: 10.1016/j.eururo.2014.11.025] [Citation(s) in RCA: 269] [Impact Index Per Article: 29.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Accepted: 11/10/2014] [Indexed: 01/18/2023]
Abstract
CONTEXT Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. OBJECTIVE The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. EVIDENCE ACQUISITION This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. EVIDENCE SYNTHESIS The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. CONCLUSIONS To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). PATIENT SUMMARY The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes.
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Affiliation(s)
- Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK.
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Douglas G Altman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
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Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. J Clin Epidemiol 2015; 68:134-43. [PMID: 25579640 DOI: 10.1016/j.jclinepi.2014.11.010] [Citation(s) in RCA: 202] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
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Affiliation(s)
- Gary S Collins
- Center for Statistics in Medicine, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Center, University of Oxford, Oxford, United Kingdom.
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Douglas G Altman
- Center for Statistics in Medicine, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Center, University of Oxford, Oxford, United Kingdom
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
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Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BJOG 2015; 122:434-43. [PMID: 25623578 DOI: 10.1111/1471-0528.13244] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
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Affiliation(s)
- G S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
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Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. Diabet Med 2015; 32:146-54. [PMID: 25600898 DOI: 10.1111/dme.12654] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/10/2014] [Indexed: 12/17/2022]
Abstract
Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study, regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
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Affiliation(s)
- G S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
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Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. Eur J Clin Invest 2015; 45:204-14. [PMID: 25623047 DOI: 10.1111/eci.12376] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Accepted: 11/10/2014] [Indexed: 12/19/2022]
Abstract
BACKGROUND Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. MATERIALS AND METHODS The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. RESULTS The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. CONCLUSIONS To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
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Affiliation(s)
- Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
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Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement. Br J Surg 2015; 102:148-58. [PMID: 25627261 DOI: 10.1002/bjs.9736] [Citation(s) in RCA: 521] [Impact Index Per Article: 57.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Accepted: 11/07/2014] [Indexed: 01/15/2023]
Abstract
BACKGROUND Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. METHODS An extensive list of items based on a review of the literature was created, which was reduced after a web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. RESULTS The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. CONCLUSION The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. A complete checklist is available at http://www.tripod-statement.org.
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Affiliation(s)
- G S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
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Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Br J Cancer 2015; 112:251-9. [PMID: 25562432 PMCID: PMC4454817 DOI: 10.1038/bjc.2014.639] [Citation(s) in RCA: 81] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health-care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
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Affiliation(s)
- G S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford OX3 7LD, UK
| | - J B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, PO Box 85500, 3508GA Utrecht, The Netherlands
| | - D G Altman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford OX3 7LD, UK
| | - K G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, PO Box 85500, 3508GA Utrecht, The Netherlands
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Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. The TRIPOD Group. Circulation 2015; 131:211-9. [PMID: 25561516 PMCID: PMC4297220 DOI: 10.1161/circulationaha.114.014508] [Citation(s) in RCA: 385] [Impact Index Per Article: 42.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. METHODS The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. RESULTS The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. CONCLUSIONS To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
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Affiliation(s)
- Gary S Collins
- From Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, United Kingdom, and Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands. The current affiliation for Drs Collins and Altman is the Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford OX3 7LD, United Kingdom. The current affiliation for Drs Reitsma and Moons is the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, the Netherlands.
| | - Johannes B Reitsma
- From Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, United Kingdom, and Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands. The current affiliation for Drs Collins and Altman is the Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford OX3 7LD, United Kingdom. The current affiliation for Drs Reitsma and Moons is the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, the Netherlands
| | - Douglas G Altman
- From Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, United Kingdom, and Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands. The current affiliation for Drs Collins and Altman is the Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford OX3 7LD, United Kingdom. The current affiliation for Drs Reitsma and Moons is the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, the Netherlands
| | - Karel G M Moons
- From Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, United Kingdom, and Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands. The current affiliation for Drs Collins and Altman is the Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford OX3 7LD, United Kingdom. The current affiliation for Drs Reitsma and Moons is the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, the Netherlands
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Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015; 162:W1-73. [PMID: 25560730 DOI: 10.7326/m14-0698] [Citation(s) in RCA: 2875] [Impact Index Per Article: 319.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.
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Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 2015; 162:55-63. [PMID: 25560714 DOI: 10.7326/m14-0697] [Citation(s) in RCA: 1668] [Impact Index Per Article: 185.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
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Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med 2015; 13:1. [PMID: 25563062 PMCID: PMC4284921 DOI: 10.1186/s12916-014-0241-z] [Citation(s) in RCA: 939] [Impact Index Per Article: 104.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2014] [Accepted: 11/14/2014] [Indexed: 02/07/2023] Open
Abstract
Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
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Affiliation(s)
- Gary S Collins
- />Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Centre for Statistics in Medicine, University of Oxford, Oxford, OX3 7LD UK
| | - Johannes B Reitsma
- />Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, the Netherlands
| | - Douglas G Altman
- />Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Centre for Statistics in Medicine, University of Oxford, Oxford, OX3 7LD UK
| | - Karel GM Moons
- />Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, the Netherlands
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Steyerberg EW, Pencina MJ, Lingsma HF, Kattan MW, Vickers AJ, Van Calster B. Assessing the incremental value of diagnostic and prognostic markers: a review and illustration. Eur J Clin Invest 2012; 42:216-28. [PMID: 21726217 PMCID: PMC3587963 DOI: 10.1111/j.1365-2362.2011.02562.x] [Citation(s) in RCA: 143] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
BACKGROUND New markers may improve prediction of diagnostic and prognostic outcomes. We review various measures to quantify the incremental value of markers over standard, readily available characteristics. METHODS Widely used traditional measures include the improvement in model fit or in the area under the receiver operating characteristic (ROC) curve (AUC). New measures include the net reclassification index (NRI) and decision-analytic measures, such as the fraction of true-positive classifications penalized for false-positive classifications [net benefit (NB)]. For illustration, we discuss a case study on the presence of residual tumour vs. benign tissue in 544 patients with testicular cancer. We assessed three tumour markers [Alpha-fetoprotein (AFP), Human chorionic gonadotropin (HCG) and Lactate dehydrogenase (LDH)] for their incremental value over currently standard clinical predictors. RESULTS AUC and R(2) values suggested adding continuous LDH and AFP whereas NB only favoured HCG as a potentially promising marker at a clinically defendable decision threshold of 20% risk. The NRI suggested reclassification potential of all three markers. CONCLUSIONS The improvement in standard discrimination measures, which focus on finding variables that might be promising across all decision thresholds, may not detect the most informative markers at a specific threshold of particular clinical relevance. When a marker is intended to support decision-making, calculation of the improvement in a decision-analytic measure, such as NB, is preferable over an overall judgment as obtained from the AUC in ROC analysis.
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