CoChat
Sign Up
Cited by 10ECG Monitoring and AnalysisCardiac electrophysiology and arrhythmiasArtificial Intelligence in Healthcare

Artificial Intelligence-Enabled Electrocardiography Identifies Severe Dyscalcemias and Has Prognostic Value

Open access
Lin, Chin, Chen, Chien-Chou, Chau, Tom, Lin, Chin-Sheng, Tsai, Shi-Hung, Lee, Ding-Jie, Lee, Chia-Cheng, Shang, Hung-Sheng +1 more·2022·Clinica Chimica Acta
DOI 10.1016/j.cca.2022.09.021
Open this paper in CoChat — free accountOpen PDF

Abstract

CONTEXT: Abnormal serum calcium concentrations affect the heart and may alter the electrocardiogram (ECG), but the detection of hypocalcemia and hypercalcemia (collectively dyscalcemia) relies on blood laboratory tests requiring turnaround time. OBJECTIVE: The study aimed to develop a bloodless artificial intelligence (AI)-enabled (ECG) method to rapidly detect dyscalcemia and analyze its possible utility for outcome prediction. METHODS: This study collected 86,731 development, 15,611 tuning, 11,105 internal validation, and 8401 external validation ECGs from electronic medical records with at least 1 ECG associated with an albumin-adjusted calcium (aCa) value within 4 h. The main outcomes were to assess the accuracy of AI-ECG to predict aCa and follow up these patients for all-cause mortality, new-onset acute myocardial infraction (AMI), and new-onset heart failure (HF) to validate the ability of AI-ECG-aCa for previvor identification. RESULTS: ECG-aCa had mean absolute errors (MAE) of 0.78/0.98 mg/dL and achieved an area under receiver operating characteristic curves (AUCs) 0.9219/0.8447 and 0.8948/0.7723 to detect severe hypercalcemia and hypocalcemia in the internal/external validation sets, respectively. Although < 20 % variance of ECG-aCa could be explained by traditional ECG features, the ECG-aCa was found to be associated with more complications. Patients with ECG-hypercalcemia but initially normal aCa were found to have a higher risk of subsequent all-cause mortality [hazard ratio (HR): 2.05, 95 % conference interval (CI): 1.55-2.70], new-onset AMI (HR: 2.88, 95 % CI: 1.72-4.83), and new-onset HF (HR: 2.02, 95 % CI: 1.38-2.97) in the internal validation set, which were also seen in external validation. CONCLUSION: The AI-ECG-aCa may help detecting severe dyscalcemia for early diagnosis and ECG-hypercalcemia also has prognostic value for clinical outcomes (all-cause mortality and new-onset AMI and HF).

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.cca.2022.09.021

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 35 · Cited by 10

Research works printed but not linked: 31

  1. A. Catalano, D. Chilà, F. Bellone, G. Nicocia, G. Martino, I. Loddo, N. Morabito, S. Benvenga, S. Loddo, Incidence of hypocalcemia and hypercalcemia in hospitalized patients: Is it changing? J. Clin. Transl. Endocrinol. 13 (2018) 9-13, https://doi.org/10.1016/j.jcte.2018.05.004. · DOI
  2. Y. Obi, R. Mehrotra, M.B. Rivara, E. Streja, C.M. Rhee, W.L. Lau, C.P. Kovesdy, K. Kalantar-Zadeh, Hidden hypercalcemia and mortality risk in incident hemodialysis patients, J. Clin. Endocrinol. Metab. 101 (6) (2016) 2440-2449, https://doi.org/10.1210/jc.2016-1369. · DOI
  3. S. Nijjer, A.K. Ghosh, S.W. Dubrey, Hypocalcaemia, long QT interval and atrial arrhythmias, BMJ Case Rep 2010 (2010) bcr0820092216. https://doi.org/ 10.1136/bcr.08.2009.2216. · DOI
  4. E. Ilveskoski, S. Sclarovsky, K. Nikus, Severe hypocalcemia simulating ST-elevation myocardial infarction, Am J Emerg Med 30(1) (2012) 256.e3-6. https://doi.org/ 10.1016/j.ajem.2010.10.021. · DOI
  5. E. Stefanaki, M. Koropuli, S. Stefanaki, A. Tsilimigaki, Atrioventricular block in preterm infants caused by hypocalcaemia: a case report and review of the literature, Eur. J. Obstet. Gynecol. Reprod. Biol. 120 (1) (2005) 115-116, https:// doi.org/10.1016/j.ejogrb.2004.09.012. · DOI
  6. P. Bjerregaard, H. Nallapaneni, I. Gussak, Short QT interval in clinical practice, J. Electrocardiol. 43 (5) (2010) 390-395, https://doi.org/10.1016/j. jelectrocard.2010.06.004. · DOI
  7. D. Manzur-Sandoval, G. Dueñas-Pérez, E. Ortiz-Brizuela, P. Valdez-Hernández, J. Oseguera-Moguel, J.L. Briseño-de la Cruz, Osborn J-wave in a patient with hypercalcemic crisis, J. Emerg. Med. 59 (2) (2020) 298-299, https://doi.org/ 10.1016/j.jemermed.2020.04.032. · DOI
  8. M. Mosseri, A. Porath, I. Ovsyshcher, D. Stone, Electrocardiographic manifestations of combined hypercalcemia and hypermagnesemia, J. Electrocardiol. 23 (3) (1990) 235-241, https://doi.org/10.1016/0022-0736(90)90162-u. · DOI
  9. Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature 521 (7553) (2015) 436-444, https://doi.org/10.1038/nature14539. · DOI · Paper page
  10. C.C. Lee, C.S. Lin, C.S. Tsai, T.P. Tsao, C.C. Cheng, J.T. Liou, W.S. Lin, C.C. Lee, J. T. Chen, C. Lin, A deep learning-based system capable of detecting pneumothorax via electrocardiogram, Eur. J. Trauma Emerg. Surg. 48 (4) (2022) 3317-3326, https://doi.org/10.1007/s00068-022-01904-3. · DOI
  11. W.T. Liu, C.S. Lin, T.P. Tsao, C.C. Lee, C.C. Cheng, J.T. Chen, C.S. Tsai, W.S. Lin, C. Lin, A deep-learning algorithm-enhanced system integrating electrocardiograms and chest X-rays for diagnosing aortic dissection, Can. J. Cardiol. 38 (2) (2022) 160-168, https://doi.org/10.1016/j.cjca.2021.09.028. · DOI
  12. Y.L. Liu, C.S. Lin, C.C. Cheng, C. Lin, A deep learning algorithm for detecting acute pericarditis by electrocardiogram, J. Pers. Med. 12 (7) (2022) 1150, https://doi. org/10.3390/jpm12071150. · DOI
  13. W.C. Liu, C.S. Lin, C.S. Tsai, T.P. Tsao, C.C. Cheng, J.T. Liou, W.S. Lin, S.M. Cheng, Y.S. Lou, C.C. Lee, C. Lin, A deep-learning algorithm for detecting acute myocardial infarction, EuroIntervention 17 (9) (2021) 765-773, https://doi.org/10.4244/eij- d-20-01155. · DOI
  14. D.W. Chang, C.S. Lin, T.P. Tsao, C.C. Lee, J.T. Chen, C.S. Tsai, W.S. Lin, C. Lin, Detecting digoxin toxicity by artificial intelligence-assisted electrocardiography, Int. J. Environ. Res. Public Health 18 (7) (2021) 3839, https://doi.org/10.3390/ ijerph18073839. · DOI
  15. C. Lin, C.S. Lin, D.J. Lee, C.C. Lee, S.J. Chen, S.H. Tsai, F.C. Kuo, T. Chau, S.H. Lin, Artificial intelligence assisted electrocardiography for early diagnosis of thyrotoxic periodic paralysis, J. Endocr. Soc. 5 (9) (2021) bvab120, https://doi.org/10.1210/ jendso/bvab120. · DOI
  16. C.S. Lin, C. Lin, W.H. Fang, C.J. Hsu, S.J. Chen, K.H. Huang, W.S. Lin, C.S. Tsai, C. C. Kuo, T. Chau, S.J. Yang, S.H. Lin, A deep-learning algorithm (ECG12Net) for detecting hypokalemia and hyperkalemia by electrocardiography: algorithm development, JMIR Med. Inform. 8 (3) (2020) e15931, https://doi.org/10.2196/ 15931. · DOI · Paper page
  17. Z.I. Attia, D.M. Harmon, E.R. Behr, P.A. Friedman, Application of artificial intelligence to the electrocardiogram, Eur. Heart J. 42 (46) (2021) 4717-4730, https://doi.org/10.1093/eurheartj/ehab649. · DOI
  18. C. Lin, T. Chau, C.S. Lin, H.S. Shang, W.H. Fang, D.J. Lee, C.C. Lee, S.H. Tsai, C. H. Wang, S.H. Lin, Point-of-care artificial intelligence-enabled ECG for dyskalemia: a retrospective cohort analysis for accuracy and outcome prediction, NPJ Digit. Med. 5 (1) (2022) 8, https://doi.org/10.1038/s41746-021-00550-0. · DOI · Paper page
  19. P. Evenepoel, B. Bammens, K. Claes, D. Kuypers, B.K. Meijers, Y. Vanrenterghem, Measuring total blood calcium displays a low sensitivity for the diagnosis of hypercalcemia in incident renal transplant recipients, Clin. J. Am. Soc. Nephrol. 5 (11) (2010) 2085-2092, https://doi.org/10.2215/cjn.02460310. · DOI
  20. C.S. Lin, Y.T. Lee, W.H. Fang, Y.S. Lou, F.C. Kuo, C.C. Lee, C. Lin, Deep learning algorithm for management of diabetes mellitus via electrocardiogram-based glycated hemoglobin (ECG-HbA1c): a retrospective cohort study, J. Pers. Med. 11 (8) (2021) 725, https://doi.org/10.3390/jpm11080725. · DOI
  21. S.v. Buuren, K. Groothuis-Oudshoorn, mice: Multivariate imputation by chained equations in R, J Stat Softw 45(3) (2010) 1-68. https://doi.org/10.18637/jss.v045. i03. · DOI · Paper page
  22. S. Turhan, M. Kilickap, S. Kilinc, ST segment elevation mimicking acute myocardial infarction in hypercalcaemia, Heart 91 (8) (2005) 999, https://doi.org/10.1136/ hrt.2003.030874. · DOI
  23. A. Abugroun, A. Tyle, F. Faizan, M. Accavitti, C. Ahmed, T. Wang, Hypercalcemia- induced ST-segment elevation mimicking acute myocardial injury: a case report and review of the literature, Case Rep. Emerg. Med. 2020 (2020) 4159526, https:// doi.org/10.1155/2020/4159526. · DOI
  24. J. Donovan, M. Jackson, Hypercalcaemia mimicking STEMI on electrocardiography, Case Rep. Med. 2010 (2010) 563572. https://doi.org/ 10.1155/2010/563572. · DOI
  25. Z.I. Attia, S. Kapa, F. Lopez-Jimenez, P.M. McKie, D.J. Ladewig, G. Satam, P. A. Pellikka, M. Enriquez-Sarano, P.A. Noseworthy, T.M. Munger, S.J. Asirvatham, C.G. Scott, R.E. Carter, P.A. Friedman, Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram, Nat. Med. 25 (1) (2019) 70-74, https://doi.org/10.1038/s41591-018-0240-2. · DOI · Paper page
  26. S. Minisola, J. Pepe, S. Piemonte, C. Cipriani, The diagnosis and management of hypercalcaemia, BMJ 350 (2015) h2723. https://doi.org/10.1136/bmj.h2723. · DOI
  27. S. Ahmad, G. Kuraganti, D. Steenkamp, Hypercalcemic crisis: a clinical review, Am. J. Med. 128 (3) (2015) 239-245, https://doi.org/10.1016/j.amjmed.2014.09.030. · DOI
  28. R. Carroll, G. Matfin, Endocrine and metabolic emergencies: hypocalcaemia, Ther. Adv. Endocrinol. Metab. 1 (1) (2010) 29-33, https://doi.org/10.1177/ 2042018810366494. · DOI
  29. E.M. Lima, A.H. Ribeiro, G.M.M. Paixão, M.H. Ribeiro, M.M. Pinto-Filho, P. R. Gomes, D.M. Oliveira, E.C. Sabino, B.B. Duncan, L. Giatti, S.M. Barreto, W. Meira Jr., T.B. Schön, A.L.P. Ribeiro, Deep neural network-estimated electrocardiographic age as a mortality predictor, Nat. Commun. 12 (1) (2021) 5117, https://doi.org/10.1038/s41467-021-25351-7. · DOI · Paper page
  30. J.M. Kwon, M.S. Jung, K.H. Kim, Y.Y. Jo, J.H. Shin, Y.H. Cho, Y.J. Lee, J.H. Ban, K. H. Jeon, S.Y. Lee, J. Park, B.H. Oh, Artificial intelligence for detecting electrolyte imbalance using electrocardiography, Ann. Noninvasive Electrocardiol. 26 (3) (2021) e12839, https://doi.org/10.1111/anec.12839. · DOI
  31. A. Kato, T. Takita, M. Furuhashi, T. Fujimoto, H. Suzuki, M. Hakamada, Y. Maruyama, Influence of the assay for measuring serum albumin on corrected total calcium in chronic hemodialysis patients, Ther. Apher. Dial 15 (6) (2011) 540-546, https://doi.org/10.1111/j.1744-9987.2011.00997.x. · DOI

Authors
Lin, C., Chen, C.-C., Chau, T., Lin, C.-S., Tsai, S.-H., Lee, D.-J., Lee, C.-C., Shang, H.-S., & Lin, S.-H.
Affiliations
  • Tri-Service General Hospital (TW)
  • Providence St. Vincent Medical Center (US)
Corresponding author
Lin, Shih-Hua
ORCID
Venue
Clinica Chimica Acta
Publisher
Elsevier BV
Published
2022-11
Volume
536
Pages
126-134
ISSN
0009-8981 (ISSN-L)
DOI
10.1016/j.cca.2022.09.021
OpenAlex
W4296940604
Cited by
10
References
35
Citations by year
2026: 4 · 2025: 4 · 2024: 1 · 2023: 1
FWCI
1.05
Type
journal article
Language
en
Open access
Licence
Locations
  • Clinica Chimica Acta (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
Ministry of Science and Technology, Taiwan (MOST 110?, CHNDMC-111-07)
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
artificial intelligence, electrocardiogram, hypocalcemia, hypercalcemia, albumin-adjusted calcium, prognostic value, all-cause mortality, acute myocardial infarction, heart failure, electrocardiographic findings, electronic health records, external validation
MeSH terms
Albumins, Artificial Intelligence, Calcium, Electrocardiography, Heart Failure, Humans, Hypercalcemia, Hypocalcemia, Prognosis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review