Using what you get: Dynamic physiologic signatures of critical illness

Critical Care Clinics

Volumn 31, Issue 1, 2015, Pages 133-164

  Holder, Andre L ^a   Clermont, Gilles ^a  

^a UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

Author keywords

Cardiopulmonary instability; Data hierarchy; Fused parameter; Machine learning; Physiologic signature

Indexed keywords

ARTERIAL OXYGEN SATURATION; ARTERIAL PRESSURE; CARDIOPULMONARY INSUFFICIENCY; CENTRAL VENOUS PRESSURE; CRITICAL ILLNESS; DIASTOLIC BLOOD PRESSURE; DISEASE COURSE; EARLY DIAGNOSIS; HEART OUTPUT; HEART RATE VARIABILITY; HEART STROKE VOLUME; HEMODYNAMIC MONITORING; HEMODYNAMIC PARAMETERS; HIGH RISK PATIENT; HUMAN; LUNG ARTERY PRESSURE; MACHINE LEARNING; MEAN ARTERIAL PRESSURE; OXYGEN SATURATION; PULSE PRESSURE; REVIEW; SURVIVAL RATE; SYSTOLIC BLOOD PRESSURE; WAVEFORM; CARDIOVASCULAR DISEASES; HEMODYNAMICS; INTENSIVE CARE; LUNG DISEASES; PHYSIOLOGIC MONITORING; PHYSIOLOGY; PROCEDURES; STATISTICAL ANALYSIS;

CARDIOVASCULAR DISEASES; CRITICAL CARE; CRITICAL ILLNESS; DATA INTERPRETATION, STATISTICAL; HEMODYNAMICS; HUMANS; LUNG DISEASES; MONITORING, PHYSIOLOGIC;

EID: 84915781135     PISSN: 07490704     EISSN: 15578232     Source Type: Journal    
DOI: 10.1016/j.ccc.2014.08.007     Document Type: Review

References (127)

1. Kauvar D.S., Lefering R., Wade C.E. Impact of hemorrhage on trauma outcome: an overview of epidemiology, clinical presentations, and therapeutic considerations. JTrauma 2006, 60:S3-S11.
2. Otero R.M., Nguyen H.B., Huang D.T., et al. Early goal directed therapy in severe sepsis and septic shock revisited: concept, controversies, and contemporary findings. Chest 2006, 130:1579-1595.
3. Rivers E.P., Nguyen B., Havstad S., et al. Early goal-directed therapy in the treatment of severe sepsis and septic shock. NEngl J Med 2001, 345:1368-1377.
4. Dellinger R.P., Levy M.M., Rhodes A., et al. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2012. Crit Care Med 2013, 41:580-637.
5. Shapiro N.I., Howell M.D., Talmor D., et al. Serum lactate as a predictor of mortality in emergency department patients with infection. Ann Emerg Med 2005, 45:524-528.
6. Trzeciak S., Chansky M.E., Dellinger R.P., et al. Operationalizing the use of serum lactate measurement for identifying high risk of death in a clinical practice algorithm for suspected severe sepsis. Acad Emerg Med 2006, 13(Suppl 1):S150-S151.
7. Pinsky M.R. Hemodynamic evaluation and monitoring in the ICU. Chest 2007, 132:2020-2029.
8. Pinsky M.R., Payen D. Functional hemodynamic monitoring. Crit Care 2005, 9:566-572.
9. American College of Surgeons Trauma Committee Advanced trauma life support for doctors 2008, American College of Surgeons, Chicago. 8th edition.
10. Giraud R., Siegenthaler N., Gayet-Ageron A., et al. ScvO2 as a marker to define fluid responsiveness. JTrauma 2011, 70:802-807.
11. Kumar A., Anel R., Bunnell E., et al. Pulmonary artery occlusion pressure and central venous pressure fail to predict ventricular filling volume, cardiac performance, or the response to volume infusion in normal subjects. Crit Care Med 2004, 32:691-699.
12. Marik P.E., Baram M., Vahid B. Does central venous pressure predict volume responsiveness? A systematic review of the literature and the tale of seven mares. Chest 2008, 134:172-178.
13. Oohashi S., Endoh H. Does central venous pressure or pulmonary capillary wedge pressure reflect the status of circulating blood volume in patient after extended transthoracic esophagectomy?. JAnesth 2005, 19:21-25.
14. Jones A.E., Shapiro N.I., Trzeciak S., et al. Lactate clearance vs. central venous oxygen saturation as goals of early sepsis therapy. JAMA 2010, 303:739-746.
15. Du W., Liu D., Wang X., et al. Combining central venous-to-arterial partial pressure of carbon dioxide difference and central venous oxygen saturation to guide resuscitation in septic shock. JCrit Care 2013, 28:1110.e1-1110.e5. 10.1016/j.jcrc.2013.07.049.
16. Vallée F., Vallet B., Mathe O., et al. Central venous-to-arterial carbon dioxide difference: an additional target for goal-directed therapy in septic shock?. Intensive Care Med 2008, 34:2218-2225.
17. Sakr Y., Vincent J., Schuerholz T., et al. Early- versus late-onset shock in European intensive care units. Shock 2007, 28:636-643.
18. Zhen W., Schorr C., Hunter K., et al. Contrasting treatment and outcome of septic shock: presentation on hospital floors versus emergency department. Clin Med J (Engl) 2010, 123:3550-3553.
19. Thom O., Taylor D.M., Wolfe R.E., et al. Pilot study of the prevalence, outcomes and detection of occult hypoperfusion in trauma patients. Emerg Med J 2010, 27:470-472.
20. Kaplan L.J., Kellum J.A. Comparison of acid-base models for prediction of hospital mortality after trauma. Shock 2008, 29:662-666.
21. Jansen T.C., van Bommel J., Mulder P.G., et al. Prognostic value of blood lactate levels: does the clinical diagnosis at admission matter?. JTrauma 2009, 66:377-385.
22. Young A., Marik P.E., Sibole S., et al. Changes in end-tidal carbon dioxide andvolumetric carbon dioxide as predictors of volume responsiveness in hemodynamically unstable patients. JCardiothorac Vasc Anesth 2013, 27:681-684.
23. Dunham C.M., Chircihella T.J., Gruber B.S., et al. In emergently ventilated trauma patients, low end-tidal CO2 and low cardiac output are associated with hemodynamic instability, hemorrhage, abnormal pupils, and death. BMC Anesthesiology 2013, 13:20. 10.1186/1471-2253-13-20.
24. Aya H.D., Cecconi M., Hamilton M., et al. Goal-directed therapy in cardiac surgery: a systematic review and meta-analysis. Br J Anaesth 2013, 110:510-517.
25. Giglio M.T., Marucci M., Testini M., et al. Goal-directed haemodynamic therapy and gastrointestinal complications in major surgery: a meta-analysis of randomized controlled trials. Br J Anaesth 2009, 103:637-646.
26. Giglio M., Dalfino L., Puntillo F., et al. Haemodynamic goal-directed therapy in cardiac and vascular surgery. A systematic review and meta-analysis. Interact Cardiovasc Thorac Surg 2013, 15:878-887.
27. Ghneim M.H., Regner J.L., Jupiter D.C., et al. Goal directed fluid resuscitation decreases time for lactate clearance and facilitates early fascial closure in damage control surgery. Am J Surg 2013, 206:995-1000.
28. Guiterrez M.C., Moore P.G., Liu H. Goal-directed therapy in intraoperative fluid and hemodynamic management. JBiomed Res 2013, 27:357-365.
29. Haas S., Eichhorn V., Hasbach T., et al. Goal-directed fluid therapy using stroke volume variation does not result in pulmonary fluid overload in thoracic surgery requiring one-lung ventilation. Crit Care Res Pract 2012, 2012:687018. 10.1155/2012/687018.
30. Lin S., Huang C., Lin H., et al. Amodified goal-directed protocol improves clinical outcomes in intensive care unit patients with septic shock: a randomized controlled trial. Shock 2006, 26:551-557.
31. Forget P., Lois F., de Kock M. Goal-directed fluid management based on the pulse oximeter-derived pleth variability index reduces lactate levels and improves fluid management. Anesth Analg 2010, 111:910-914.
32. Hata J.S., Scotts C., Shelsky C., et al. Reduced mortality with noninvasive hemodynamic monitoring of shock. JCrit Care 2011, 26:224.e1-224.e8.
33. Benes J., Chytra I., Altmann P., et al. Intraoperative fluid optimization using stroke volume variation in high-risk surgical patients: results of prospective randomized study. Crit Care 2010, 14:R118. 10.1186/cc9070.
34. Salzwedel C., Puig J., Carstens A., et al. Perioperative goal-directed hemodynamic therapy based on radial arterial pulse pressure variation and continuous cardiac index trending reduces postoperative complications after major abdominal surgery: a multi-center, prospective, randomized study. Crit Care 2013, 17:R191. Available at:. http://ccforum.com/content/17/5/R191.
35. Lopes M.R., Oliveira M.A., Pereira V.O., et al. Goal-directed fluid management based on pulse pressure variation monitoring during high-risk surgery: a pilot randomized controlled trial. Crit Care 2007, 11:R100. Available at:. http://ccforum.com/content/11/5/R100.
36. Brienza N., Giglio M.T., Marucci M., et al. Does perioperative hemodynamic optimization protect function in surgical patients? A meta-analytic study. Crit Care Med 2009, 37:2079-2090.
37. Dalfino L., Giglio M.T., Puntillo F., et al. Haemodynamic goal-directed therapy and postoperative infections: earlier is better. A systematic review and meta-analysis. Crit Care 2011, 15:R154. 10.1186/cc10284.
38. Zhang J., Qiao H., He Z., et al. Intraoperative fluid management in open gastrointestinal surgery: goal-directed versus restrictive. Clinics (Sao Paulo) 2012, 67:1149-1155.
39. Zheng H., Guo H., Ye J., et al. Goal-directed fluid therapy in gastrointestinal surgery in older coronary heart disease patients: randomized trial. World J Surg 2013, 37:2820-2829.
40. Barochia A.V., Chi X., Vitberg D., et al. Bundled care for septic shock: an analysis of clinical trials. Crit Care Med 2010, 38:668-678.
41. Chamberlain D.J., Willis E.W., Bersten A.B. The severe sepsis bundles as processes of care: a meta-analysis. Aust Crit Care 2011, 24:229-243.
42. Shim H.J., Jang J.Y., Lee S.H., et al. The effects of positive balance on the outcomes of critically ill noncardiac postsurgical patients: a retrospective cohort study. JCrit Care 2014, 29:43-48.
43. Boyd J.H., Forbes J., Nakada T., et al. Fluid resuscitation in septic shock: a positive fluid balance and elevated central venous pressure are associated with increased mortality. Crit Care Med 2011, 39:259-265.
44. Futier E., Constantin J., Petit A., et al. Conservative vs. restrictive individualized goal-directed fluid replacement strategy in major abdominal surgery. Arch Surg 2010, 145:1193-1200.
45. Lobo S.M., Ronchi L.S., Oliveira N.E., et al. Restrictive strategy of intraoperative fluid maintenance during optimization of oxygen delivery decreases major complications after high-risk surgery. Crit Care 2011, 15:R226. 10.1186/cc10466.
46. Hayes M.A., Timmins A.C., Yau E.H., et al. Elevation of systemic oxygen delivery in the treatment of critically ill patients. NEngl J Med 1994, 330:1717-1722.
47. Pearse R., Dawson D., Fawcett J., et al. Early goal-directed therapy after major surgery reduces complications and duration of hospital stay. A randomised, controlled trial. Crit Care 2005, 9:R687-R693. Available at:. http://ccforum.com/content/9/6/R687.
48. Fairchild K.D., O'Shea T.M. Heart rate characteristics: physiomarkers for detection of late-onset neonatal sepsis. Clin Perinatol 2010, 37:581-598.
49. Godin P.J., Buchman T.G. Uncoupling of biological oscillators: a complementary hypothesis concerning the pathogenesis of multiple organ dysfunction syndrome. Crit Care Med 1996, 24:1107-1116.
50. Pinsky M.R. Complexity modeling: identify instability early. Crit Care Med 2010, 38(Suppl):S649-S655.
51. Seely A.J., Christou N.V. Multiple organ dysfunction syndrome: exploring the paradigm of complex nonlinear systems. Crit Care Med 2000, 28:2193-2200.
52. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation 1996, 93:1043-1065.
53. Fathizadeh P., Shoemaker W.C., Wo C.C., et al. Autonomic activity in trauma patients based on variability of heart rate and respiratory rate. Crit Care Med 2004, 32:1300-1305.
54. Omboni S., Parati G., Di Rienzo M., et al. Blood pressure and heart rate in autonomic disorders: a critical review. Clin Auton Res 1996, 6:171-182.
55. Kamath M.V., Fallen E.L. Power spectral analysis of heart rate variability: a noninvasive signature of cardiac autonomic function. Crit Rev Biomed Eng 1993, 21:245-311.
56. Kasaoka S., Nakahara T., Kawamura Y., et al. Real-time monitoring of heart rate variability in critically ill patients. JCrit Care 2010, 25:313-316.
57. Kleiger R.E., Miller J.P., Bigger J.T., et al. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol 1987, 59:256-262.
58. Buchan C.A., Bravi A., Seely A.J. Variability analysis and the diagnosis, management, and treatment of sepsis. Curr Infect Dis Rep 2012, 14:512-521.
59. Norris P.R., Morris J.A., Ozdas A., et al. Heart rate variability predicts trauma patient outcomes as early as 12 hours: implications for military and civilian triage. JSurg Res 2005, 129:122-128.
60. Chen W., Kuo C. Characteristics of heart rate variability can predict impending shock in emergency department patients with sepsis. Acad Emerg Med 2007, 14:392-397.
61. Barnaby D., Ferrick K., Kaplan D.T., et al. Heart rate variability in emergency department patients with sepsis. Acad Emerg Med 2002, 9:661-670.
62. Ahmad S., Ramsay T., Huebsch L., et al. Continuous multi-parameter heart rate variability analysis heralds onset of sepsis in adults. PLoS One 2009, 4:e6642. 10.1371/journal.pone.0006642.
63. Batchinsky A.I., Cooke W.H., Kuusela T., et al. Loss of complexity characterizes the heart rate response to experimental hemorrhagic shock in swine. Crit Care Med 2007, 35:519-525.
64. Cooke W.H., Salinas J., Convertino V.A., et al. Heart rate variability and its association with mortality in prehospital trauma patients. JTrauma 2006, 60:363-370.
65. Bradley B., Green G.C., Batkin I., et al. Feasibility of continuous multiorgan variability analysis in the intensive care unit. JCrit Care 2012, 27:218.e9-218.e20. 10.1016/j.jcrc.2011.09.009.
66. Cannesson M. Arterial pressure variation and goal-directed fluid therapy. JCardiothorac Vasc Anesth 2010, 24:487-497.
67. Barbeito A., Mark J.B. Arterial and central venous pressure monitoring. Anesthesiol Clin 2006, 24:717-735.
68. Michard F., Boussat S., Chemla D., et al. Relation between respiratory changes in arterial pulse pressure and fluid responsiveness in septic patients with acute circulatory failure. Am J Respir Crit Care Med 2000, 162:134-138.
69. Huang C., Fu J., Hu H., et al. Prediction of fluid responsiveness in acute respiratory distress syndrome patients ventilated with low tidal volume and high positive end-expiratory pressure. Crit Care Med 2008, 36:2810-2816.
70. Zhang Z., Lu B., Sheng X., et al. Accuracy of stroke volume variation in predicting fluid responsiveness: a systematic review and meta-analysis. JAnesth 2011, 25:904-916.
71. Schereen T.W., Wiesenack C., Gerlach H., et al. Goal-directed intraoperative fluid therapy guided by stroke volume and its variation in high-risk surgical patients: a prospective randomized multicentre study. JClin Monit Comput 2013, 27:225-233.
72. Belloni L., Pisano A., Natale A., et al. Assessment of fluid-responsiveness parameters for off-pump coronary artery bypass surgery: a comparison among LiDCO, transesophageal echocardiography, and pulmonary artery catheter. JCardiothorac Vasc Anesth 2008, 22:243-248.
73. Kramer A., Zygan D., Hawes H., et al. Pulse pressure variation predicts fluid responsiveness following coronary artery bypass surgery. Chest 2004, 126:1563-1568.
74. Natalini G., Rosano A., Taranto M., et al. Arterial versus plethysmographic dynamic indices to test responsiveness for testing fluid administration in hypotensive patients: a clinical trial. Anesth Analg 2006, 103:1478-1484.
75. Reuter D.A., Felbinger T.W., Kilger E., et al. Optimizing fluid therapy in mechanically ventilated patients after cardiac surgery by on-line monitoring of left ventricular stroke volume variations. Comparison with aortic systolic pressure variations. Br J Anaesth 2002, 88:124-126.
76. Tavernier B., Makhotine O., Lebuffe G., et al. Systolic pressure variation as a guide to fluid therapy in patients with sepsis-induced hypotension. Anesthesiology 1998, 89:1313-1321.
77. Jeleazcov C., Krajinovic L., Munster T., et al. Precision and accuracy of a new device (CNAP) for continuous non-invasive arterial pressure monitoring: assessment during general anesthesia. Br J Anaesth 2010, 105:264-272.
78. Kako H., Corridore M., Rice J., et al. Accuracy of the CNAP monitor, a noninvasive continuous blood pressure device, in providing beat-to-beat blood pressure readings in pediatric patients weighing 20-40 kilograms. Paediatr Anaesth 2013, 23:989-993.
79. Martina J.R., Westerhof B.E., van Goudoever J., et al. Noninvasive continuous arterial blood pressure monitoring with Nexfin. Anesthesiology 2012, 116:1092-1103.
80. Cannesson M., Besnard C., Durand P.G., et al. Relation between respiratory variations in pulse oximetry plethysmographic waveform amplitude and arterial pulse pressure in ventilated patients. Crit Care 2005, 9:R562-R568. Available at:. http://ccforum.com/content/9/5/R562.
81. Yin J.Y., Ho K.M. Use of plethysmographic variability index derived from the Massimo pulse oximeter to predict fluid or preload responsiveness: a systematic review and meta-analysis. Anaesthesia 2012, 67:777-783.
82. O'Rourke M.F. Time domain analysis of the arterial pulse in clinical medicine. Med Biol Eng Comput 2009, 47:119-129.
83. Fujita Y., Hayashi D., Wada S., et al. Central venous pulse pressure analysis using an R-synchronized pressure measurement system. JClin Monit Comput 2006, 20:385-389.
84. Roy S., Couture P., Qizilbash B., et al. Hemodynamic pressure waveform analysis in predicting volume responsiveness. JCardiothorac Vasc Anesth 2013, 27:676-680.
85. Scheuer M.L., Wilson S.B. Data analysis for continuous EEG monitoring in the ICU: seeing the forest and the trees. JClin Neurophysiol 2004, 21:353-378.
86. De Melis M., Morbiducci U., Rietzschel E.R., et al. Blood pressure waveform analysis by means of wavelet transform. Med Biol Eng Comput 2009, 47:165-173.
87. Cohen M.J. Use of models in identification and prediction of physiology in critically ill surgical patients. Br J Surg 2012, 99:487-493.
88. Dubrawski A. Detection of events in multiple streams of surveillance data: multivariate, multi-stream and multi-dimensional approaches. Infectious disease informatics and biosurveillance 2011, 145-171. Springer, New York. D. Zeng, H. Chen, C. Castillo-Chavez (Eds.).
89. Rasouli K., Hsieh W.W., Cannon A.J. Daily streamflow forecasting by machine learning methods with weather and climate inputs. JHydrol 2012, 414-415:284-293.
90. Buckeridge D.L. Outbreak detection through automated surveillance: a review of determinants of detection. JBiomed Inform 2007, 40:370-379.
91. Chang Y., Yeh M., Li Y., et al. Predicting hospital-acquired infections by scoring system with simple parameters. PLoS One 2011, 6:e23137. 10.1371/journal.pone.0023137.
92. Dumont T.M., Rughani A.I., Tranmer B.I. Prediction of symptomatic cerebral vasospasm after aneurysmal subarachnoid hemorrhage with an artificial neural network: feasibility and comparison with logistic regression models. World Neurosurg 2011, 75:57-63.
93. Paul M., Nielsen A.D., Goldberg E., et al. Prediction of specific pathogens in patients with sepsis: evaluation of TREAT, a computerized decision support system. JAntimicrob Chemother 2007, 59:1204-1207.
94. Purwento, Eswaran C., Logeswaran R., et al. Prediction models for early risk detection of cardiovascular event. JMed Syst 2012, 36:521-531.
95. Ong M.E., Ng C.H., Goh K., et al. Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score. Crit Care 2012, 16:R108. Available at:. http://ccforum.com/content/16/3/R108.
96. Clermont G., Angus D.C., DiRusso S.M., et al. Predicting hospital mortality for patients in the intensive care unit: a comparison of artificial neural networks with logistic regression models. Crit Care Med 2001, 29:291-296.
97. Silva A., Cortez P., Santos M.F., et al. Rating organ failure via adverse events using data mining in the intensive care unit. Artif Intell Med 2008, 43:179-193.
98. Ribas V.J., López J.C., Ruiz-Sanmartin A., et al. Severe sepsis mortality prediction with relevance vector machines. Conf Proc IEEE Eng Med Biol Soc 2011, 2011:100-103.
99. Jaimes F., Farbiarz J., Alvarez D., et al. Comparison between logistic regression and neural networks to predict death in patients with suspected sepsis in the emergency room. Crit Care 2005, 9:R150. Available at:. http://ccforum.com/content/9/2/R150.
100. Gultepe E., Green J.P., Nguyen H., et al. From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system. JAm Med Inform Assoc 2014, 21:315-325. 10.1136/amiajnl-2013-001815.
101. Schurink C.A., Lucas P.J., Hoepelman I.M., et al. Computer-assisted decision support for the diagnosis and treatment of infectious diseases in intensive care units. Lancet Infect Dis 2005, 5:302-312.
102. Andersson B., Andersson R., Ohlsson M., et al. Prediction of severe acute pancreatitis at admission to hospital using artificial neural networks. Pancreatology 2011, 11:328-335.
103. Tang C.H., Middleton P.M., Savkin A.V., et al. Non-invasive classification of severe sepsis and systemic inflammatory response syndrome using a nonlinear support vector machine: a preliminary study. Physiol Meas 2010, 31:775-793.
104. Xiao Y., Griffin P., Lake D.E., et al. Nearest-neighbor and logistic regression analyses of clinical and heart rate characteristics in the early diagnosis of neonatal sepsis. Med Decis Making 2010, 30:258-266.
105. Glass T.F., Knapp J., Ambrun P., et al. Use of artificial intelligence to identify cardiovascular collapse in a model of hemorrhagic shock. Crit Care Med 2004, 32:450-456.
106. Convertino V.A., Ryan K.L., Rickards C.A., et al. Physiological and medical monitoring for en route care of combat casualties. JTrauma 2008, 64:S342-S353.
107. Tarrasenko L., Hann A., Young D. Integrated monitoring and analysis for early warning of patient deterioration. Br J Anaesth 2006, 97:64-68.
108. Watkinson P.J., Barber V.S., Price J.D., et al. Arandomized controlled trial of theeffect of continuous electronic physiological monitoring on the adverse event rate in high risk medical and surgical patients. Anaesthesia 2006, 61:1031-1039.
109. Hravnak M., DeVita M.A., Clontz A., et al. Cardiorespiratory instability before and after implementing an integrated monitoring system. Crit Care Med 2011, 39:65-72.
110. Shoemaker W.C., Bayard D.S., Wo C.C., et al. Outcome prediction in chest injury by a mathematical search and display program. Chest 2005, 128:2739-2748.
111. Shoemaker W.C., Bayard D.S., Botnen A., et al. Mathematical program for outcome prediction and therapeutic support for trauma beginning within 1 hr of admission: a preliminary report. Crit Care Med 2005, 33:1499-1506.
112. Convertino V.A., Grudic G., Mulligan J., et al. Estimation of individual-specific progression to impending cardiovascular instability using arterial waveforms. JAppl Physiol (1985) 2013, 115:1196-1202.
113. Rothman M.J., Rothman S.I., Beals J. Development and validation of a continuous measure of patient condition using the electronic medical record. JBiomed Inform 2013, 46:837-848.
114. Bradley E.H., Yakusheva O., Horwitz L.I., et al. Identifying patients at risk for unplanned readmission. Med Care 2013, 51:761-766.
115. Finlay G.D., Rothman M.J., Smith R.A. Measuring the modified early warning score and the Rothman index: advantages of utilizing the electronic medical record in an early warning system. JHosp Med 2014, 9:116-119.
116. Tepas J.J., Rimar J.M., Hsiao A.L., et al. Automated analysis of electronic medical record data reflects the physiology of operative complications. Surgery 2013, 154:918-926.
117. Fairchild K.D. Predictive monitoring for early detection of sepsis in neonatal ICUpatients. Curr Opin Pediatr 2013, 25:172-179. 10.1097/MOP.0b013e32835e8fe6.
118. Fairchild K.D., Aschner J.L. HeRO monitoring to reduce mortality in NICU patients. Res Rep Neonatol 2012, 2:65-76.
119. Moorman J.R., Delos J.B., Flower A.A., et al. Cardiovascular oscillations at the bedside: early diagnosis of neonatal sepsis using heart rate characteristics monitoring. Physiol Meas 2011, 32:1821-1832.
120. Griffin M.P., Moorman J.R. Toward the early diagnosis of neonatal sepsis andsepsis-like illness using novel heart rate analysis. Pediatrics 2001, 107:97-104.
121. Griffin M.P., O'Shea T.M., Bissonette E.A., et al. Abnormal heart rate characteristics preceding neonatal sepsis and sepsis-like illness. Pediatr Res 2003, 53:920-926.
122. Moorman J.R., Carlo W.A., Kattwinkel J., et al. Mortality reduction by heart rate variability characteristic monitoring in very low birth weight neonates: a randomized controlled trial. JPediatr 2011, 159:900-906.
123. Griffin M.P., Lake D.E., O'Shea T.M., et al. Heart rate characteristics and clinical signs in late-onset neonatal sepsis. Pediatr Res 2007, 61:222-227.
124. Seely A.J., Green G.C., Bravi A. Continuous multiorgan variability monitoring in critically ill patients-complexity science at the bedside. Conf Proc IEEE Eng Med Biol Soc 2011, 2011:5503-5506.
125. Green G.C., Bradley B., Bravi A., et al. Continuous multiorgan variability analysis to track severity of organ failure in critically ill patients. JCrit Care 2013, 28:879.e1-879.e11. 10.1016/j.jcrc.2013.04.001.
126. Das A., Wong R.C. Prediction of outcome in acute lower gastrointestinal hemorrhage: role of artificial neural network. Eur J Gastroenterol Hepatol 2007, 19:1064-1069.
127. Vallée F., Fourcade O., Marty P., et al. The hemodynamic "target": a visual tool of goal-directed therapy for septic patients. Clinics (Sao Paulo) 2007, 62:447-454.