Литература

Прогнозирование с помощью шкал

1. Белялов Ф.И. Возможности и перспективы систем поддержки принятия клинических решений // Клиническая медицина. 2021. T. 99. № 11–12. C. 602–607.
2. Белялов Ф.И. Есть ли будущее у персонифицированной медицины? // Клиническая медицина. 2014. № 6. С. 73–74.
3. Белялов Ф.И. Индивидуализированная медицина в клинической практике // Клиническая медицина. 2020. T. 98. № 1. С. 61–67.
4. Белялов Ф.И. Использование шкал прогноза в клинической медицине // Российский кардиологический журнал. 2016. № 12. С. 23–27.
5. Белялов Ф.И. Исследование механизмов нестабильного течения стенокардии // Сибирский медицинский журнал. 2001. № 1. С. 32–36.
6. Белялов Ф.И. Клиника продромального периода при обострениях стенокардии // Клиническая медицина. 2001. № 8. С. 28–30.
7. Белялов Ф.И. Психосоматические и средовые факторы при нестабильной стенокардии: дис. ... д-ра мед. наук. СПб, 2002. 222 c.
8. Белялов Ф.И., Исхакова Г.И. Связи гелиогеофизических факторов и течения нестабильной стенокардии // Терапевтический архив. 2002. № 9. С. 34–36.
9. Белялов Ф.И., Куклин С.Г. Вариабельность сердечного ритма при многодневном наблюдении за течением нестабильной стенокардии // Кардиология. 2002. № 1. С. 48–51.
10. Гаврилов Д.В., Серова Л.М., Корсаков И.Н. и др. Предсказание сердечно-
    сосудистых событий при помощи комплексной оценки факторов риска с использованием методов машинного обучения // Врач. 2020. № 5. С. 41–45.
11. Симаненков В.И., Белялов Ф.И., Куклин С.Г. Психосоматические отношения при нестабильной стенокардии // Социальная и клиническая психиатрия. 2004. № 3. С. 19–22.
12. Adler E., Voors A., Klein L. et al. Improving risk prediction in heart failure using machine learning // Eur. J. Heart Failure. 2020. Vol. 22. N. 1. P. 139–147.
13. Anderson K., Ross H.J., Austin P.C. et al. Health Care Use Before First Heart Failure Hospitalization // JACC: Heart Failure. 2020. Vol. 8. N. 12. P. 1024–1034.
14. Arkes H.R., Aberegg S.K., Arpin K.A. Analysis of Physicians’ Probability Estimates of a Medical Outcome Based on a Sequence of Events // JAMA Netw. Open. 2022. Vol. 5. N. 6. P. e2218804.
15. Aspberg S., Chang Y., Atterman A. et al. Comparison of the ATRIA, CHADS2, and CHA2DS2-VASc stroke risk scores in predicting ischaemic stroke in a large Swedish cohort of patients with atrial fibrillation // Eur. Heart J. 2016. Vol. 37. N. 42. P. 3203.
16. Beck A.-J.C., Hagemeijer A., Tortolani B. et al. Comparing an unstructured risk stratification to published guidelines in acute coronary syndromes // West. J. Emerg. Med. 2015. Vol. 16. N. 5. P. 683–689.
17. Brugnara G., Neuberger U., Mahmutoglu M.A. et al. Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning // Stroke. 2020. N. 12. P. 3541–3551.
18. Chao T.-F., Lip G.Y.H., Liu C.-J. et al. Relationship of aging and incident comorbidities to stroke risk in patients with atrial fibrillation // J. Am. Coll. Cardiol. 2018. Vol. 71. N. 2. P. 122.
19. Chew D.P., Hyun K., Morton E. et al. Objective Risk Assessment vs Standard Care for Acute Coronary Syndromes: A Randomized Trial // JAMA Cardiol. 2021. Vol. 6. N. 3. P. 304–313.
20. Chia Y.C., Lim H.M., Ching S.M. Validation of the pooled cohort risk score in an Asian population — a retrospective cohort study // BMC Cardiovasc. Disord. 2014. Vol. 14. P. 163.
21. Churpek M.M., Yuen T.C., Winslow C. et al. Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards // Crit. Care Med. 2016. Vol. 44. N. 2. P. 368–374.
22. Collins G.S., Ogundimu E.O., Cook J.A. et al. Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic model // Stat. Med. 2016. Vol. 35. N. 23. P. 4124–4135.
23. Collins G.S., Reitsma J.B., Altman D.G., Moons K.G.M. Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). The TRIPOD Statement // Circulation. 2015. Vol. 131. P. 211–219.
24. Dalton J.E., Perzynski A.T., Zidar D.A. et al. Accuracy of Cardiovascular Risk Prediction Varies by Neighborhood Socioeconomic Position: A Retrospective Cohort Study // Ann. Intern. Med. 2017. Vol. 167. P. 456–464.
25. Deo R.C. Machine learning in medicine // Circulation. 2015. Vol. 132. N. 20. P. 1920.
26. Fang W.F., Yang K.Y., Wu C.L. et al. Application comparison of scoring indices to predict outcomes in patients with healthcare associated pneumonia // Crit. Care. 2011. Vol. 15. N. 1. P. R32.
27. Goldenberg I., Vyas A.K., Hall W.J. et al. Risk stratification for primary implantation of a cardioverter-defibrillator in patients with ischemic left ventricular dysfunction // J. Am. Coll. Cardiol. 2008. 51. N. 3. P. 288–296.
28. Han D., Beecy A., Anchouche K. et al. Risk Reclassification With Coronary Compu­ted Tomography Angiography-Visualized Nonobstructive Coronary Artery Disease According to 2018 American College of Cardiology/American Heart Association Cholesterol Guidelines // Am. J. Card. 2019. Vol. 124. N. 9. P. 1397–1405.
29. Hijazi M., Aljohani S., Alqahtani F. et al. Perception of the Risk of Stroke and the Risks and Benefits of Oral Anticoagulation for Stroke Prevention in Patients With Atrial Fibrillation: A Cross-Sectional Study // Mayo Clinic Proceedings. 2019. Vol. 94. N. 6. P. 1015–1023.
30. Horne B.D., May H.T., Muhlestein J.B. et al. Exceptional mortality prediction by risk scores from common laboratory tests // Am. J. Med. 2009. Vol. 122. N. 6. P. 550–558.
31. Hu W.-S., Lin C.-L. CHA2DS2-VASc score in the prediction of ischemic bowel disease among patients with atrial fibrillation: Insights from a nationwide cohort // Int. J. Cardiol. 2017. Vol. 235. P. 56–60.
32. Koliscak L., Maynor L. Pharmacologic prophylaxis against venous throm­boembolism in hospitalized patients with cirrhosis and associated coagu­lo­pathies // Am. J. Health Syst. Pharm. 2012. Vol. 69. P. 658–663.
33. König S., Pellissier V., Hohenstein S. et al. Machine learning algorithms for claims data-based prediction of in-hospital mortality in patients with heart failure // ESC Heart Failure. 2021. Vol. 8. N. 4. P. 3026–3036.
34. Korsakov I., Gusev A., Kuznetsova T. et al. Deep and machine learning models to improve risk prediction of cardiovascular disease using data extraction from electronic health records // Eur. Heart J. 2019. Vol. 40. Suppl. 1.
35. Kurtul A., Acikgoz S.K. Validation of the CHA2DS2-VASc Score in Predicting Coronary Atherosclerotic Burden and In-Hospital Mortality in Patients With Acute Coronary Syndrome // Am. J. Card. 2017. Vol. 120. N. 1. P. 8–14.
36. Liew S.M., Doust J., Glasziou P. Cardiovascular risk scores do not account for
    the effect of treatment: a review // Heart. 2011. Vol. 97. N. 9. P. 689–697.
37. Loke Y.K., Kwok C.S., Niruban A., Myint P.K. Value of severity scales in predicting mortality from community-acquired pneumonia: systematic review and meta-analysis // Thorax. 2010. Vol. 65. N. 10. P. 884–890.
38. Loring Z., Mehrotra S., Piccini J. et al. Machine learning does not improve upon traditional regression in predicting outcomes in atrial fibrillation: an analysis of the ORBIT-AF and GARFIELD-AF registries // EP Europace. 2020. N. 11. P. 1635–1644.
39. Marzouka G., Rivner H., Mehta V. et al. The CHA2DS2-VASc Score for Risk Stratification of Stroke in Heart Failure With-vs-Without Atrial Fibrillation // Am. J. Card. 2021. Vol. 155. P. 72–77.
40. May H.T., Reiss–Brennan B., Brunisholz K.D., Horne B.D. Clinically feasible stratification of 3-year chronic disease risk in primary care: the mental health integration risk score // Psychosomatics. 2017. Vol. 58. N. 4. P. 395–405.
41. Modi R., Patted S.V., Halkati P.C. et al. CHA2DS2-VASc-HSF score — New predictor of severity of coronary artery disease in 2976 patients // Int. J. Cardiol. 2017. Vol. 228. P. 1002–1006.
42. Müller-Riemenschneider F., Holmberg C., Rieckmann N. et al. Barriers to routine risk-score use for healthy primary care patients: survey and qualitative study // Arch. Int. Med. 2010. Vol. 170. N. 8. P. 719–724.
43. Muntner P., Whelton P.K. Using Predicted Cardiovascular Disease Risk in Conjunction With Blood Pressure to Guide Antihypertensive Medication Treatment // J. Am. Coll. Cardiol. 2017. Vol. 69. N. 19. P. 2446.
44. Nashef S.A.M., Roques F., Sharples L.D. et al. EuroSCORE II // Eur. J. Cardiothorac. Surg. 2012. Vol. 41. N. 4. P. 734–745.
45. Obermeyer Z., Emanuel E.J. Predicting the Future — Big Data, Machine Learning, and Clinical Medicine // N. Engl. J. Med. 2016. Vol. 375. N. 13.
    P. 1216–1219.
46. Orvin K., Bental T., Assali A. et al. Usefulness of the CHA2DS2-VASc Score to Predict Adverse Outcomes in Patients Having Percutaneous Coronary Intervention // Am. J. Cardiol. 2016. Vol. 117. N. 9. P. 1433–1438.
47. Orvin K., Levi A., Landes U. et al. Usefulness of the CHA2DS2-VASc Score to Predict Outcome in Patients Who Underwent Transcatheter Aortic Valve Implantation // Am. J. Card. 2018. Vol. 121. N. 2. P. 241–248.
48. Pate A., Emsley R., Ashcroft D.M. et al. The uncertainty with using risk predic­tion models for individual decision making: an exemplar cohort study examining the prediction of cardiovascular disease in English primary care // BMC Med. 2019. Vol. 17. N. 1. P. 134.
49. Pate A., van Staa T., Emsley R. et al. An assessment of the potential miscalibration of cardiovascular disease risk predictions caused by a secular trend in cardiovascular disease in England // BMC Med. Res. Meth. 2020. N. 1. P. 289.
50. Phillips R., Xu J., Peterson L. et al. Impact of Cardiovascular Risk on the Relative Benefit and Harm of Intensive Treatment of Hypertension // J. Am. Coll. Card. 2018. N. 15. P. 1601–1610.
51. Pirracchio R., Petersen M.L., Carone M. et al. Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA) a population-based study // Lancet Respir. Med. 2015. Vol. 3. N. 1. P. 42–52.
52. Pisters R., Lane D.A., Nieuwlaat R. et al. A novel user-friendly score (HAS-BLED) to assess one-year risk of major bleeding in atrial fibrillation patients: The Euro Heart Survey // Chest. 2010. Vol. 138. N. 5. P. 1093–1100.
53. Ren Y., Loftus T.J., Datta S. et al. Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform // JAMA Netw. Open. 2022. Vol. 5. N. 5.
    P. e2211973.
54. Rücker V., Keil U., Fitzgerald A.P. et al. Predicting 10-Year Risk of Fatal Cardiovascular Disease in Germany: An Update Based on the SCORE-Deutschland Risk Charts // PLoS One. 2016. Vol. 11. N. 9. P. e0162188.
55. Stähli B., Wischnewsky M., Jakob P. et al. Predictive value of the age, creatinine, and ejection fraction (ACEF) score in patients with acute coronary syndromes // Int. J. Card. 2018. Vol. 270. P. 7–13.
56. Steinberg B.A., Shrader P., Kim S. et al. How well does physician risk as-sessment predict stroke and bleeding in atrial fibrillation? Results from the ORBIT-AF // Am. Heart J. 2016. Vol. 181. P. 145–152.
57. Studziński K., Tomasik T., Krzysztoń J. et al. Effect of using cardiovascular risk scoring in routine risk assessment in primary prevention of cardiovascular disease: an overview of systematic reviews // BMC Cardiovasc. Disord. 2019. Vol. 19. N. 1. P. 11.
58. Subherwal S., Bach R.G., Chen A.Y. et al. Baseline Risk of Major Bleeding in Non-ST-Segment-Elevation Myocardial Infarction: The CRUSADE Bleeding Score // Circulation. 2009. Vol. 119 N. 14. P. 1873–1882.
59. Than M., Flaws D., Sanders S. et al. Development and validation of the Emergency Department Assessment of Chest pain Score and 2 h accelerated diagnostic protocol // Emerg. Med. Australas. 2014. Vol. 26. N. 1. P. 34–44.
60. Topaz G., Pereg D., Shuvy M. et al. Preadmission CHA2DS2-VASc score and outcome of patients with acute cerebrovascular events // Int. J. Cardiol. 2017. Vol. 244. P. 277–281.
61. Van den Ham H.A., Klungel O.H., Singer D.E. et al. Comparative Performance of ATRIA, CHADS2, and CHA2DS2-VASc Risk Scores Predicting Stroke in Patients With Atrial Fibrillation // J. Am. Coll. Cardiol. 2015. Vol. 66. N. 17.
    P. 1851–1859.
62. Weiner D.E., Tighiouart H., Elsayed E.F. et al. The Framingham Predictive Instrument in Chronic Kidney Disease // J. Am. Coll. Cardiol. 2007. Vol. 50.
    P. 217–224.
63. Weng S.F., Reps J., Kai J. et al. Can machine-learning improve cardiovascular risk prediction using routine clinical data? // PLOS ONE. 2017. Vol. 12. N. 4.
    P. e0174944.
64. Wessler B.S., Nelson J., Park J.G. et al. External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature // Circ. Cardiovasc. Qual. Outcomes. 2021. Vol. 14. N. 8. P. e007858.
65. Wessler B.S., Paulus J., Lundquist C.M. et al. Tufts PACE Clinical Predictive Model Registry: update 1990 through 2015 // Diagn. Progn. Res. 2017. N. 1.
    P. 20.
66. Wick J.P., Turin T.C., Faris P.D. et al. A Clinical Risk Prediction Tool for 6-Month Mortality After Dialysis Initiation Among Older Adults // Am. J. Kidn. Dis. 2017. Vol. 69. N. 5. P. 568–575.
67. Wykrzykowska J.J. et al. Value of Age, Creatinine, and Ejection Fraction (ACEF Score) in Assessing Risk in Patients Undergoing Percutaneous Coronary Interventions in the ‘All-Comers' LEADERS Trial // Circulation: Cardiovascular Interventions. 2011. N. 4. P. 47–56.
68. Younis A., Goldberger J., Kutyifa V. et al. Predicted benefit of an implantable cardioverter-defibrillator: the MADIT-ICD benefit score // Eur. Heart J. 2021. N. 17. P. 1676–1684.
69. Zhou L.Z., Yang X.B., Guan Y. et al. Development and Validation of a Risk Score for Prediction of Acute Kidney Injury in Patients With Acute Decompensated Heart Failure: A Prospective Cohort Study in China // J. Am. Heart Assoc. 2016. Vol. 5. N. 11.
70. Zhu W., Fu L., Ding Y. et al. Meta-analysis of ATRIA versus CHA2DS2-VASc for predicting stroke and thromboembolism in patients with atrial fibrillation // Int. J. Cardiol. 2017. Vol. 227. P. 436–442.