- Email: [email protected]
Next-generation sequencing for detection of somatic mosaicism in autosomal dominant polycystic kidney disease
commentary
Next-generation sequencing for detection of somatic mosaicism in autosomal dominant polycystic kidney disease Olivier Devuyst1,2 and York Pei3,4 Mosaicism is defined as the presence of 2 genetically different populations of cells in a single organism, resulting from a mutation during early embryogenesis. Hopp et al. characterized mosaicism in 20 unresolved ADPKD families, using next-generation sequencing techniques with DNA isolated from blood cells. Mosaicism may be involved in 1% of ADPKD families, and it may explain some atypical disease phenotypes. Kidney International (2020) 97, 261–263; https://doi.org/10.1016/j.kint.2019.11.019 Copyright ª 2020, International Society of Nephrology. Published by Elsevier Inc. All rights reserved.
see clinical investigation on page 370
A
utosomal dominant polycystic kidney disease (ADPKD) is the most common inherited nephropathy, accounting for up to 10% of patients requiring renal replacement therapy. ADPKD is genetically heterogeneous, with mutations in PKD1 and PKD2 accounting for 85% and 15% of the clinically enriched and genetically resolved cases, respectively. These genes encode 2 membrane proteins, polycystin-1 and polycystin-2, which are expressed in distinct domains and play multiple roles in the cell.1 Mutations of at least 5 genes (i.e., GANAB, PRKCSH, ALG8, SEC61B, and SEC63), which encode proteins in the
1
Department of Physiology, Mechanisms of Inherited Kidney Diseases Group, University of Zurich, Zurich, Switzerland; 2Division of Nephrology, UCLouvain Medical School, Brussels, Belgium; 3Division of Nephrology, University Health Network, Toronto, Ontario, Canada; and 4 Division of Nephrology, University of Toronto, Toronto, Ontario, Canada Correspondence: Olivier Devuyst, Department of Physiology, Mechanisms of Inherited Kidney Diseases Group, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland. E-mail: [email protected]; or York Pei, Division of Nephrology, University Health Network, Toronto, 585 University Avenue, 8N838 Toronto, Ontario M5G2N2, Canada. E-mail: [email protected] Kidney International (2020) 97, 251–265
endoplasmic reticulum involved in maturation and proper surface localization of integral membrane proteins including polycystin-1 and polycystin2, have been shown to cause a mild form of ADPKD with variable cystic liver disease severity.2 Rare, atypical cystic kidney diseases associated with tubulointerstitial damage have also been linked to mutations in DNAJB11, coding for a cofactor of the chaperone BiP that ensures the proper folding and assembly of membrane or secretory proteins in the endoplasmic reticulum, and HNF1B, coding for a transcription factor that operates in multiple organs and in kidney tubular cells.3 In the presence of a positive family history, the diagnosis of ADPKD is typically based on age-dependent kidney cyst counts by kidney imaging. Genetic testing for ADPKD may be useful in a number of clinical scenarios including polycystic kidney disease with: (i) early and severe manifestation, (ii) marked intrafamilial disease variability, (iii) lack of apparent family history, (iv) atypical renal imaging, and (v) syndromic presentation. Moreover, genetic testing may be used for disease exclusion in a younger at-risk subjects for potential kidney donation and for
prenatal diagnosis.4 The standard genetic testing for PKD1 relies on longrange polymerase chain reactions followed by nested polymerase chain reactions to generate PKD1-specific amplicons and screen the entire gene (46 exons, the first 33 being duplicated in 6 pseudogenes), whereas PKD2 (15 exons) can be covered by standard Sanger sequencing. New approaches based on next-generation sequencing (NGS) coupled to targeted sequence enrichment (with locus-specific, long-range PCR or DNA capture) are increasingly used to screen PKD1 and PKD2.4 To date, more than 1250 pathogenic germline mutations in PKD1—and more than 200 in PKD2—are listed in the Mayo PKD database (https://pkdb.mayo.edu/). The mutations, which are mostly unique, occur through the length of both genes, without specific clustering. Screening for mutations in PKD1 and PKD2 in large patient cohorts allowed the demonstration of robust genotypephenotype correlations in ADPKD. In general, patients harboring a mutation in PKD1 have larger kidneys and, on average, reach end-stage kidney disease 20 years faster than those harboring a PKD2 mutation. The type of PKD1 mutation is also important: proteintruncating mutations (nonsense, frameshift) or large deletions are associated with a more severe kidney phenotype than nontruncating (in-frame insertions/deletions, missense) mutations.5 Despite comprehensive testing of individuals with polycystic kidneys, it is estimated that up to 15% of cases remain genetically unsolved.5 These individuals with no mutations detected (NMD) in both PKD1 and PKD2 often report no apparent family history, and they may present with milder disease and/or with an atypical kidney imaging pattern (e.g., unilateral or asymmetric cystic kidneys).6 Mosaicism, defined as the presence of 2 genetically different populations of cells in a single organism resulting from a mutation during early embryogenesis, is important to consider in such cases as it will characterize the prognosis and risk of disease transmission in the family considered (Figure 1). The diagnosis of 261
commentary
Fertilized egg from which all body cells arise Mixed somatic and gonadal mosaicism PKD1 mosaic family segregating the pathogenic variant II-2, 48y, CT
Fertilized egg divides into many cells to form an embryo
390010 I
As the cells continue to divide, the DNA in 1 of the cells becomes altered
1 The PKD1 gene is mutated G A
The cells with a normal or with a mutated PKD1 gene expand and contribute to the formation of organs and tissues within the growing embryo
2
II 1
2
3
4
III-1, 35y, MRI
III 1
Mosaicism: the fetus contains 2 types of cells, either with the normal or with the mutated PKD1 gene
2
Somatic mosaicism PKD1 mosaic family not segregating the pathogenic variant II-3, 37y, CT M375 I
Somatic Gonadal
Low-grade
High-grade
1
2
Mixed II 1
2
3
4
M646
II-3, 54y, CT
I 1
2
II 1
2
3 4–6 1
Types of mosaicism
Figure 1 | Molecular mechanism and types of mosaicism in autosomal dominant polycystic kidney disease (ADPKD) (PKD1). The pedigrees are among those presented by Hopp et al.8 Symbols: red, mosaic; black, typical ADPKD; gray, equivocal or unknown. The kidney scans obtained by computed tomography (CT) or magnetic resonance imaging (MRI) are illustrated for the indicated individuals (age in years [y]).
mosaicism is technically challenging and frequently missed by Sanger sequencing, due to the dilution of the mutation signal from admixture of normal and mutant cells—a difficulty that could be overcome by the NGS technique with high read depth.7 Only a handful of ADPKD families with mosaicism have been reported thus far, all identified by the transmission of the germline mutation to an affected offspring. In this issue, Hopp et al.8 (2020) characterized mosaicism in 20 unresolved ADPKD families, using 2 state-ofthe-art NGS techniques with DNA isolated from blood cells.8 In 5 families, the 262
PKD1 mosaicism was somatic but also germinal, because the pathogenic variant was transmitted to the next generation (mixed mosaicism). Fifteen families had a somatic mosaic PKD1 pathogenic variant that was not shown to be transmitted, that is, affecting a single individual (Figure 1). Comparison of the estimated glomerular filtration rate (eGFR) and height adjusted total kidney volume (htTKV) indicated that the mosaic PKD1 cases had a less severe disease (i.e., significantly better eGFR and smaller htTKV) than nonmosaic PKD1 individuals with similar mutation types. Of note, there was no correlation
between the level of mosaicism and disease severity, probably because the level of mutant allele in blood cells did not reflect that level in the kidney. For the 6 cases where both NGS methods were employed, there was good agreement in the mutant allele level (present in 1.3%– 20.6% of the reads). Altogether, on the basis of the NGS methodology used, the authors estimate that mosaicism may be involved in approximately 1% of typical ADPKD families and approximately 10% of unsolved cases with no family history. These results demonstrate that NGS can be used to readily detect mosaicism in unsolved ADPKD cases. This Kidney International (2020) 97, 251–265
commentary
information is clinically important, because it explains the usually milder kidney disease in mosaics than expected for the mutation type, and the likelihood for more severe disease in the affected offspring compared with the parent in transmitted cases (25% of the families studied by Hopp et al.; Figure 1).8 It should also be noted that all cases of mosaicism were detected in the PKD1 gene, possibly reflecting the complexity (i.e., high guanine-cytosine content) and large size (i.e., approximately 13 kb of coding sequence) of this gene possibly rendering it to be more prone to de novo mutations. Importantly, a number of the unilateral or markedly asymmetric polycystic kidney cases in these ADPKD cohorts remained unsolved after this NGS screen for mosaicism. Moving forward, targeted NGS with high average read depth (i.e., 5000–10,000) will continue to be the method of choice for screening patients with polycystic kidney disease and suspected somatic mosaicism. In addition, screening their DNA samples isolated from non-blood tissues (i.e., epithelial cells from urine and buccal mucosa) and lowering the variant call threshold to 0.5%–1% of the reads may improve the diagnostic yield. However, more false-positive calls are also expected when the variant call threshold is lowered to less than 1%. This technical challenge potentially can be minimized by incorporating molecular barcoding in the targeted NGS that tags the input DNA to create a family of reads containing the variants of interest.9 Altogether, the studies of Hopp et al. illustrate how advances in genetic testing technologies yield important information for the clinical management of patients with ADPKD and substantiate a better understanding of the disease spectrum. DISCLOSURE
All the authors declared no competing interests. ACKNOWLEDGMENTS
OD is supported by the Swiss National Centre of Competence in Research Kidney Control of Homeostasis (NCCR Kidney.CH) program, the Swiss National Science Kidney International (2020) 97, 251–265
Foundation (310030_189044), and the Rare Disease Zurich (radiz) priority program of the University of Zurich. YP is supported by the Canadian Institutes of Health Research Strategy for Patient Oriented Research (SPOR) program grant in Chronic Kidney Disease (CAN-Solve-CKD). REFERENCES 1. Ong AC, Devuyst O, Knebelmann B, Walz G, ERA-EDTA Working Group for Inherited Kidney Diseases. Autosomal dominant polycystic kidney disease: the changing face of clinical management. Lancet. 2015;385: 1993–2002. 2. Besse W, Dong K, Choi JM, et al. Isolated polycystic liver disease genes define effectors of polycystin-1 function. J Clin Invest. 2017;127:1772–1785. 3. Devuyst O, Olinger E, Weber S, et al. Autosomal dominant tubulointerstitial kidney disease. Nat Rev Dis Primers. 2019;5:60.
4. Lanktree MB, Iliuta IA, Haghighi A, et al. Evolving role of genetic testing for the clinical management of autosomal dominant polycystic kidney disease. Nephrol Dial Transplant. 2019;34:1453–1460. 5. Hwang Y-H, Conklin J, Chan W, et al. Refining genotype-phenotype correlation in autosomal dominant polycystic kidney disease. J Am Soc Nephrol. 2016;27:1861–1868. 6. Iliuta IA, Kalatharan V, Wang K, et al. Polycystic kidney disease without an apparent family history. J Am Soc Nephrol. 2017;28:2768–2776. 7. Gajecka M. Unrevealed mosaicism in the nextgeneration sequencing era. Mol Genet Genomics. 2016;291:513–530. 8. Hopp K, Cornec-Le Gall E, Senum SR, et al. Detection and characterization of mosaicism in autosomal dominant polycystic kidney disease. Kidney Int. 2020;97:370–382. 9. Kinde I, Wu J, Papadopoulos N, et al. Detection and quantification of rare mutations with massively parallel sequencing. PNAS. 2011;108:9530–9535.
Considerations for advancing nephrology research and practice through natural language processing Sharidan K. Parr1,2 and Glenn T. Gobbel1,3,4 Much of medical data is buried in the free text of clinical notes and not captured by structured data, such as administrative codes. Natural language processing (NLP) can locate and use information that resides in unstructured free text. Chan et al. demonstrate that NLP is sensitive for identifying symptoms in hemodialysis patients. These findings highlight the benefit NLP may bring to nephrology and should prompt discussion of important considerations for NLP system design and implementation. Kidney International (2020) 97, 263–265; https://doi.org/10.1016/j.kint.2019.12.001 Copyright ª 2020, International Society of Nephrology. Published by Elsevier Inc. All rights reserved.
see clinical investigation on page 383 1
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA; 2Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; 3Veteran Affairs Tennessee Valley Healthcare System, Nashville, Tennessee, USA; and 4Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA Correspondence: Sharidan K. Parr, Department of Biomedical Informatics, Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 1475, Nashville, Tennessee 37203, USA. E-mail: [email protected]
E
lectronic health record systems contain a plethora of data collected through routine clinical care. Regulatory agencies in multiple countries have adapted laws and programs that facilitated widespread electronic health record adoption, such as The Health Information Technology for Economic and Clinical Health (HITECH) Act and the meaningful use incentive program in the United States.1 As a result, more data are available for analysis, facilitating creation of large observational data sets to answer 263
Next-generation sequencing for detection of somatic mosaicism in autosomal dominant polycystic kidney disease Olivier Devuyst1,2 and York Pei3,4 Mosaicism is defined as the presence of 2 genetically different populations of cells in a single organism, resulting from a mutation during early embryogenesis. Hopp et al. characterized mosaicism in 20 unresolved ADPKD families, using next-generation sequencing techniques with DNA isolated from blood cells. Mosaicism may be involved in 1% of ADPKD families, and it may explain some atypical disease phenotypes. Kidney International (2020) 97, 261–263; https://doi.org/10.1016/j.kint.2019.11.019 Copyright ª 2020, International Society of Nephrology. Published by Elsevier Inc. All rights reserved.
see clinical investigation on page 370
A
utosomal dominant polycystic kidney disease (ADPKD) is the most common inherited nephropathy, accounting for up to 10% of patients requiring renal replacement therapy. ADPKD is genetically heterogeneous, with mutations in PKD1 and PKD2 accounting for 85% and 15% of the clinically enriched and genetically resolved cases, respectively. These genes encode 2 membrane proteins, polycystin-1 and polycystin-2, which are expressed in distinct domains and play multiple roles in the cell.1 Mutations of at least 5 genes (i.e., GANAB, PRKCSH, ALG8, SEC61B, and SEC63), which encode proteins in the
1
Department of Physiology, Mechanisms of Inherited Kidney Diseases Group, University of Zurich, Zurich, Switzerland; 2Division of Nephrology, UCLouvain Medical School, Brussels, Belgium; 3Division of Nephrology, University Health Network, Toronto, Ontario, Canada; and 4 Division of Nephrology, University of Toronto, Toronto, Ontario, Canada Correspondence: Olivier Devuyst, Department of Physiology, Mechanisms of Inherited Kidney Diseases Group, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland. E-mail: [email protected]; or York Pei, Division of Nephrology, University Health Network, Toronto, 585 University Avenue, 8N838 Toronto, Ontario M5G2N2, Canada. E-mail: [email protected] Kidney International (2020) 97, 251–265
endoplasmic reticulum involved in maturation and proper surface localization of integral membrane proteins including polycystin-1 and polycystin2, have been shown to cause a mild form of ADPKD with variable cystic liver disease severity.2 Rare, atypical cystic kidney diseases associated with tubulointerstitial damage have also been linked to mutations in DNAJB11, coding for a cofactor of the chaperone BiP that ensures the proper folding and assembly of membrane or secretory proteins in the endoplasmic reticulum, and HNF1B, coding for a transcription factor that operates in multiple organs and in kidney tubular cells.3 In the presence of a positive family history, the diagnosis of ADPKD is typically based on age-dependent kidney cyst counts by kidney imaging. Genetic testing for ADPKD may be useful in a number of clinical scenarios including polycystic kidney disease with: (i) early and severe manifestation, (ii) marked intrafamilial disease variability, (iii) lack of apparent family history, (iv) atypical renal imaging, and (v) syndromic presentation. Moreover, genetic testing may be used for disease exclusion in a younger at-risk subjects for potential kidney donation and for
prenatal diagnosis.4 The standard genetic testing for PKD1 relies on longrange polymerase chain reactions followed by nested polymerase chain reactions to generate PKD1-specific amplicons and screen the entire gene (46 exons, the first 33 being duplicated in 6 pseudogenes), whereas PKD2 (15 exons) can be covered by standard Sanger sequencing. New approaches based on next-generation sequencing (NGS) coupled to targeted sequence enrichment (with locus-specific, long-range PCR or DNA capture) are increasingly used to screen PKD1 and PKD2.4 To date, more than 1250 pathogenic germline mutations in PKD1—and more than 200 in PKD2—are listed in the Mayo PKD database (https://pkdb.mayo.edu/). The mutations, which are mostly unique, occur through the length of both genes, without specific clustering. Screening for mutations in PKD1 and PKD2 in large patient cohorts allowed the demonstration of robust genotypephenotype correlations in ADPKD. In general, patients harboring a mutation in PKD1 have larger kidneys and, on average, reach end-stage kidney disease 20 years faster than those harboring a PKD2 mutation. The type of PKD1 mutation is also important: proteintruncating mutations (nonsense, frameshift) or large deletions are associated with a more severe kidney phenotype than nontruncating (in-frame insertions/deletions, missense) mutations.5 Despite comprehensive testing of individuals with polycystic kidneys, it is estimated that up to 15% of cases remain genetically unsolved.5 These individuals with no mutations detected (NMD) in both PKD1 and PKD2 often report no apparent family history, and they may present with milder disease and/or with an atypical kidney imaging pattern (e.g., unilateral or asymmetric cystic kidneys).6 Mosaicism, defined as the presence of 2 genetically different populations of cells in a single organism resulting from a mutation during early embryogenesis, is important to consider in such cases as it will characterize the prognosis and risk of disease transmission in the family considered (Figure 1). The diagnosis of 261
commentary
Fertilized egg from which all body cells arise Mixed somatic and gonadal mosaicism PKD1 mosaic family segregating the pathogenic variant II-2, 48y, CT
Fertilized egg divides into many cells to form an embryo
390010 I
As the cells continue to divide, the DNA in 1 of the cells becomes altered
1 The PKD1 gene is mutated G A
The cells with a normal or with a mutated PKD1 gene expand and contribute to the formation of organs and tissues within the growing embryo
2
II 1
2
3
4
III-1, 35y, MRI
III 1
Mosaicism: the fetus contains 2 types of cells, either with the normal or with the mutated PKD1 gene
2
Somatic mosaicism PKD1 mosaic family not segregating the pathogenic variant II-3, 37y, CT M375 I
Somatic Gonadal
Low-grade
High-grade
1
2
Mixed II 1
2
3
4
M646
II-3, 54y, CT
I 1
2
II 1
2
3 4–6 1
Types of mosaicism
Figure 1 | Molecular mechanism and types of mosaicism in autosomal dominant polycystic kidney disease (ADPKD) (PKD1). The pedigrees are among those presented by Hopp et al.8 Symbols: red, mosaic; black, typical ADPKD; gray, equivocal or unknown. The kidney scans obtained by computed tomography (CT) or magnetic resonance imaging (MRI) are illustrated for the indicated individuals (age in years [y]).
mosaicism is technically challenging and frequently missed by Sanger sequencing, due to the dilution of the mutation signal from admixture of normal and mutant cells—a difficulty that could be overcome by the NGS technique with high read depth.7 Only a handful of ADPKD families with mosaicism have been reported thus far, all identified by the transmission of the germline mutation to an affected offspring. In this issue, Hopp et al.8 (2020) characterized mosaicism in 20 unresolved ADPKD families, using 2 state-ofthe-art NGS techniques with DNA isolated from blood cells.8 In 5 families, the 262
PKD1 mosaicism was somatic but also germinal, because the pathogenic variant was transmitted to the next generation (mixed mosaicism). Fifteen families had a somatic mosaic PKD1 pathogenic variant that was not shown to be transmitted, that is, affecting a single individual (Figure 1). Comparison of the estimated glomerular filtration rate (eGFR) and height adjusted total kidney volume (htTKV) indicated that the mosaic PKD1 cases had a less severe disease (i.e., significantly better eGFR and smaller htTKV) than nonmosaic PKD1 individuals with similar mutation types. Of note, there was no correlation
between the level of mosaicism and disease severity, probably because the level of mutant allele in blood cells did not reflect that level in the kidney. For the 6 cases where both NGS methods were employed, there was good agreement in the mutant allele level (present in 1.3%– 20.6% of the reads). Altogether, on the basis of the NGS methodology used, the authors estimate that mosaicism may be involved in approximately 1% of typical ADPKD families and approximately 10% of unsolved cases with no family history. These results demonstrate that NGS can be used to readily detect mosaicism in unsolved ADPKD cases. This Kidney International (2020) 97, 251–265
commentary
information is clinically important, because it explains the usually milder kidney disease in mosaics than expected for the mutation type, and the likelihood for more severe disease in the affected offspring compared with the parent in transmitted cases (25% of the families studied by Hopp et al.; Figure 1).8 It should also be noted that all cases of mosaicism were detected in the PKD1 gene, possibly reflecting the complexity (i.e., high guanine-cytosine content) and large size (i.e., approximately 13 kb of coding sequence) of this gene possibly rendering it to be more prone to de novo mutations. Importantly, a number of the unilateral or markedly asymmetric polycystic kidney cases in these ADPKD cohorts remained unsolved after this NGS screen for mosaicism. Moving forward, targeted NGS with high average read depth (i.e., 5000–10,000) will continue to be the method of choice for screening patients with polycystic kidney disease and suspected somatic mosaicism. In addition, screening their DNA samples isolated from non-blood tissues (i.e., epithelial cells from urine and buccal mucosa) and lowering the variant call threshold to 0.5%–1% of the reads may improve the diagnostic yield. However, more false-positive calls are also expected when the variant call threshold is lowered to less than 1%. This technical challenge potentially can be minimized by incorporating molecular barcoding in the targeted NGS that tags the input DNA to create a family of reads containing the variants of interest.9 Altogether, the studies of Hopp et al. illustrate how advances in genetic testing technologies yield important information for the clinical management of patients with ADPKD and substantiate a better understanding of the disease spectrum. DISCLOSURE
All the authors declared no competing interests. ACKNOWLEDGMENTS
OD is supported by the Swiss National Centre of Competence in Research Kidney Control of Homeostasis (NCCR Kidney.CH) program, the Swiss National Science Kidney International (2020) 97, 251–265
Foundation (310030_189044), and the Rare Disease Zurich (radiz) priority program of the University of Zurich. YP is supported by the Canadian Institutes of Health Research Strategy for Patient Oriented Research (SPOR) program grant in Chronic Kidney Disease (CAN-Solve-CKD). REFERENCES 1. Ong AC, Devuyst O, Knebelmann B, Walz G, ERA-EDTA Working Group for Inherited Kidney Diseases. Autosomal dominant polycystic kidney disease: the changing face of clinical management. Lancet. 2015;385: 1993–2002. 2. Besse W, Dong K, Choi JM, et al. Isolated polycystic liver disease genes define effectors of polycystin-1 function. J Clin Invest. 2017;127:1772–1785. 3. Devuyst O, Olinger E, Weber S, et al. Autosomal dominant tubulointerstitial kidney disease. Nat Rev Dis Primers. 2019;5:60.
4. Lanktree MB, Iliuta IA, Haghighi A, et al. Evolving role of genetic testing for the clinical management of autosomal dominant polycystic kidney disease. Nephrol Dial Transplant. 2019;34:1453–1460. 5. Hwang Y-H, Conklin J, Chan W, et al. Refining genotype-phenotype correlation in autosomal dominant polycystic kidney disease. J Am Soc Nephrol. 2016;27:1861–1868. 6. Iliuta IA, Kalatharan V, Wang K, et al. Polycystic kidney disease without an apparent family history. J Am Soc Nephrol. 2017;28:2768–2776. 7. Gajecka M. Unrevealed mosaicism in the nextgeneration sequencing era. Mol Genet Genomics. 2016;291:513–530. 8. Hopp K, Cornec-Le Gall E, Senum SR, et al. Detection and characterization of mosaicism in autosomal dominant polycystic kidney disease. Kidney Int. 2020;97:370–382. 9. Kinde I, Wu J, Papadopoulos N, et al. Detection and quantification of rare mutations with massively parallel sequencing. PNAS. 2011;108:9530–9535.
Considerations for advancing nephrology research and practice through natural language processing Sharidan K. Parr1,2 and Glenn T. Gobbel1,3,4 Much of medical data is buried in the free text of clinical notes and not captured by structured data, such as administrative codes. Natural language processing (NLP) can locate and use information that resides in unstructured free text. Chan et al. demonstrate that NLP is sensitive for identifying symptoms in hemodialysis patients. These findings highlight the benefit NLP may bring to nephrology and should prompt discussion of important considerations for NLP system design and implementation. Kidney International (2020) 97, 263–265; https://doi.org/10.1016/j.kint.2019.12.001 Copyright ª 2020, International Society of Nephrology. Published by Elsevier Inc. All rights reserved.
see clinical investigation on page 383 1
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA; 2Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; 3Veteran Affairs Tennessee Valley Healthcare System, Nashville, Tennessee, USA; and 4Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA Correspondence: Sharidan K. Parr, Department of Biomedical Informatics, Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 1475, Nashville, Tennessee 37203, USA. E-mail: [email protected]
E
lectronic health record systems contain a plethora of data collected through routine clinical care. Regulatory agencies in multiple countries have adapted laws and programs that facilitated widespread electronic health record adoption, such as The Health Information Technology for Economic and Clinical Health (HITECH) Act and the meaningful use incentive program in the United States.1 As a result, more data are available for analysis, facilitating creation of large observational data sets to answer 263