Lactate and pH as Independent Biomarkers for Prognosticating Meaningful Post-out-of-Hospital Cardiac Arrest Outcomes: A Systematic Review and Meta-Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Protocol and Registration
2.2. Eligibility Criteria and Outcomes
2.3. Information Sources and Search Strategy
2.4. Study Selection
2.5. Data Collection
2.6. Risk of Bias for Individual Studies
2.7. Data Synthesis and Analysis
2.7.1. Mean Analysis
2.7.2. Odds Ratio Analysis
3. Results
3.1. Identified Studies
3.2. Overview of Included Studies
3.2.1. Lactate and Survival Analysis
3.2.2. Lactate and Neurological Outcome Analysis
3.2.3. pH and Survival Analysis
3.2.4. pH and Neurological Outcome Analysis
3.2.5. Risk of Bias
4. Discussion
Limitations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AUC | Area under the curve |
CPR | Cardiopulmonary resuscitation |
CARES | Cardiac Arrest Registry to Enhance Survival |
CPC | Cerebral Performance Category |
ECPR | Extracorporeal cardiopulmonary resuscitation |
EMR | Electronic medical record |
EMS | Emergency medical services |
GOS | Glasgow outcome scale |
HR | Hazards ratio |
IHCA | In-hospital cardiac arrest |
IQR | Interquartile range |
mRS | Modified Rankin Scale |
NOS | Newcastle–Ottawa scale |
OHCA | Out-of-hospital cardiac arrest |
OR | Odds ratio |
PICO | Population, intervention, comparison, outcome |
PRISMA | Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
ROSC | Return of spontaneous circulation |
SD | Standard deviation |
Appendix A
Appendix A.1. Lactate and Survival Analysis
Appendix A.2. Lactate and Neurological Outcome Analysis
Appendix A.3. pH and Survival Analysis
Appendix A.4. pH and Neurological Outcome Analysis
References
- Virani, S.S.; Alonso, A.; Aparicio, H.J.; Benjamin, E.J.; Bittencourt, M.S.; Callaway, C.W.; Carson, A.P.; Chamberlain, A.M.; Cheng, S.; Delling, F.N.; et al. Heart Disease and Stroke Statistics-2021 Update: A Report from the American Heart Association. Circulation 2021, 143, e254–e743. [Google Scholar] [CrossRef] [PubMed]
- Tsao, C.W.; Aday, A.W.; Almarzooq, Z.I.; Anderson, C.A.M.; Arora, P.; Avery, C.L.; Baker-Smith, C.M.; Beaton, A.Z.; Boehme, A.K.; Buxton, A.E.; et al. Heart Disease and Stroke Statistics-2023 Update: A Report from the American Heart Association. Circulation 2023, 147, e93–e621. [Google Scholar] [CrossRef] [PubMed]
- Garcia, R.A.; Girotra, S.; Jones, P.G.; McNally, B.; Spertus, J.A.; Chan, P.S.; CARES Surveillance Group. Variation in Out-of-Hospital Cardiac Arrest Survival Across Emergency Medical Service Agencies. Circ. Cardiovasc. Qual. Outcomes 2022, 15, e008755. [Google Scholar] [CrossRef] [PubMed]
- Lilja, G.; Nilsson, G.; Nielsen, N.; Friberg, H.; Hassager, C.; Koopmans, M.; Kuiper, M.; Martini, A.; Mellinghoff, J.; Pelosi, P.; et al. Anxiety and Depression among Out-of-Hospital Cardiac Arrest Survivors. Resuscitation 2015, 97, 68–75. [Google Scholar] [CrossRef]
- Wilder Schaaf, K.P.; Artman, L.K.; Peberdy, M.A.; Walker, W.C.; Ornato, J.P.; Gossip, M.R.; Kreutzer, J.S.; Virginia Commonwealth University ARCTIC Investigators. Anxiety, Depression, and PTSD Following Cardiac Arrest: A Systematic Review of the Literature. Resuscitation 2013, 84, 873–877. [Google Scholar] [CrossRef]
- Sekhon, M.S.; Ainslie, P.N.; Griesdale, D.E. Clinical Pathophysiology of Hypoxic Ischemic Brain Injury after Cardiac Arrest: A “Two-Hit” Model. Crit. Care 2017, 21, 90. [Google Scholar] [CrossRef]
- Hermansen, A.S.; Joshi, V.L.; Wagner, M.K.; Dieperink, K.B.; Zwisler, A.-D.; Borregaard, B.; DANCAS research network. Caregiver Strain among Relatives of Out-of-Hospital Cardiac Arrest Survivors; the DANCAS Relative Survey. Resuscitation 2024, 201, 110298. [Google Scholar] [CrossRef]
- Damluji, A.A.; Al-Damluji, M.S.; Pomenti, S.; Zhang, T.J.; Cohen, M.G.; Mitrani, R.D.; Moscucci, M.; Myerburg, R.J. Health Care Costs After Cardiac Arrest in the United States. Circ. Arrhythm. Electrophysiol. 2018, 11, e005689. [Google Scholar] [CrossRef]
- Marinšek, M.; Sinkovič, A.; Šuran, D. Neurological Outcome in Patients after Successful Resuscitation in Out-of-Hospital Settings. Bosn. J. Basic Med. Sci. 2020, 20, 389–395. [Google Scholar] [CrossRef]
- Mazzeffi, M.; Curley, J.; Gallo, P.; Stombaugh, D.K.; Roach, J.; Lunardi, N.; Yount, K.; Thiele, R.; Glance, L.; Naik, B. Variation in Hospitalization Costs, Charges, and Lengths of Hospital Stay for Coronavirus Disease 2019 Patients Treated with Venovenous Extracorporeal Membrane Oxygenation in the United States: A Cohort Study. J. Cardiothorac. Vasc. Anesth. 2023, 37, 1449–1455. [Google Scholar] [CrossRef]
- De Fazio, C.; Skrifvars, M.B.; Søreide, E.; Grejs, A.M.; Di Bernardini, E.; Jeppesen, A.N.; Storm, C.; Kjaergaard, J.; Laitio, T.; Rasmussen, B.S.; et al. Quality of Targeted Temperature Management and Outcome of Out-of-Hospital Cardiac Arrest Patients: A Post Hoc Analysis of the TTH48 Study. Resuscitation 2021, 165, 85–92. [Google Scholar] [CrossRef] [PubMed]
- Szarpak, L.; Filipiak, K.J.; Mosteller, L.; Jaguszewski, M.; Smereka, J.; Ruetzler, K.; Ahuja, S.; Ladny, J.R. Survival, Neurological and Safety Outcomes after out of Hospital Cardiac Arrests Treated by Using Prehospital Therapeutic Hypothermia: A Systematic Review and Meta-Analysis. Am. J. Emerg. Med. 2021, 42, 168–177. [Google Scholar] [CrossRef] [PubMed]
- Harhash, A.A.; May, T.; Hsu, C.-H.; Seder, D.B.; Dankiewicz, J.; Agarwal, S.; Patel, N.; McPherson, J.; Riker, R.; Soreide, E.; et al. Incidence of Cardiac Interventions and Associated Cardiac Arrest Outcomes in Patients with Nonshockable Initial Rhythms and No ST Elevation Post Resuscitation. Resuscitation 2021, 167, 188–197. [Google Scholar] [CrossRef] [PubMed]
- Nikolaou, N.I.; Netherton, S.; Welsford, M.; Drennan, I.R.; Nation, K.; Belley-Cote, E.; Torabi, N.; Morrison, L.J.; International Liaison Committee on Resuscitation Advanced Life Support Task Force (ILCOR). A Systematic Review and Meta-Analysis of the Effect of Routine Early Angiography in Patients with Return of Spontaneous Circulation after Out-of-Hospital Cardiac Arrest. Resuscitation 2021, 163, 28–48. [Google Scholar] [CrossRef]
- Kern, K.B.; Radsel, P.; Jentzer, J.C.; Seder, D.B.; Lee, K.S.; Lotun, K.; Janardhanan, R.; Stub, D.; Hsu, C.-H.; Noc, M. Randomized Pilot Clinical Trial of Early Coronary Angiography Versus No Early Coronary Angiography After Cardiac Arrest Without ST-Segment Elevation: The PEARL Study. Circulation 2020, 142, 2002–2012. [Google Scholar] [CrossRef]
- Lemkes, J.S.; Janssens, G.N.; van der Hoeven, N.W.; Jewbali, L.S.D.; Dubois, E.A.; Meuwissen, M.M.; Rijpstra, T.A.; Bosker, H.A.; Blans, M.J.; Bleeker, G.B.; et al. Coronary Angiography After Cardiac Arrest Without ST Segment Elevation: One-Year Outcomes of the COACT Randomized Clinical Trial. JAMA Cardiol. 2020, 5, 1358–1365. [Google Scholar] [CrossRef]
- Downing, J.; Al Falasi, R.; Cardona, S.; Fairchild, M.; Lowie, B.; Chan, C.; Powell, E.; Pourmand, A.; Tran, Q.K. How Effective Is Extracorporeal Cardiopulmonary Resuscitation (ECPR) for out-of-Hospital Cardiac Arrest? A Systematic Review and Meta-Analysis. Am. J. Emerg. Med. 2022, 51, 127–138. [Google Scholar] [CrossRef]
- Carr, C.; Carson, K.A.; Millin, M.G. Acidemia Detected on Venous Blood Gas After Out-of-Hospital Cardiac Arrest Predicts Likelihood to Survive to Hospital Discharge. J. Emerg. Med. 2020, 59, e105–e111. [Google Scholar] [CrossRef]
- Cocchi, M.N.; Salciccioli, J.; Yankama, T.; Chase, M.; Patel, P.V.; Liu, X.; Mader, T.J.; Donnino, M.W. Predicting Outcome After Out-of-Hospital Cardiac Arrest: Lactate, Need for Vasopressors, and Cytochrome c. J. Intensive Care Med. 2020, 35, 1483–1489. [Google Scholar] [CrossRef]
- Donnino, M.W.; Andersen, L.W.; Giberson, T.; Gaieski, D.; Abella, B.; Peberdy, M.A.; Rittenberger, J.C.; Callaway, C.W.; Ornato, J.; Clore, J.; et al. Initial Lactate and Lactate Change in Post-Cardiac Arrest: A Multi-Center Validation Study. Crit. Care Med. 2014, 42, 1804–1811. [Google Scholar] [CrossRef]
- Momiyama, Y.; Yamada, W.; Miyata, K.; Miura, K.; Fukuda, T.; Fuse, J.; Kikuno, T. Prognostic Values of Blood pH and Lactate Levels in Patients Resuscitated from Out-of-Hospital Cardiac Arrest. Acute Med. Surg. 2017, 4, 25–30. [Google Scholar] [CrossRef] [PubMed]
- Higgins, J.; Thomas, J. Cochrane Handbook for Systematic Reviews of Interventions. Available online: https://training.cochrane.org/handbook/current (accessed on 18 March 2025).
- Wells, G.A.; Shea, B.; O’Connell, D.; Peterson, J.; Welch, V.; Losos, M.; Tugwell, P. The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses. Available online: https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp (accessed on 18 March 2025).
- Luo, D.; Wan, X.; Liu, J.; Tong, T. Optimally Estimating the Sample Mean from the Sample Size, Median, Mid-Range, and/or Mid-Quartile Range. Stat. Methods Med. Res. 2018, 27, 1785–1805. [Google Scholar] [CrossRef] [PubMed]
- Wan, X.; Wang, W.; Liu, J.; Tong, T. Estimating the Sample Mean and Standard Deviation from the Sample Size, Median, Range and/or Interquartile Range. BMC Med. Res. Methodol. 2014, 14, 135. [Google Scholar] [CrossRef] [PubMed]
- R Core Team. R: A Language and Environment for Statistical Computing; R Core Team: Vienna, Austria, 2024. [Google Scholar]
- Balduzzi, S.; Rücker, G.; Schwarzer, G. How to Perform a Meta-Analysis with R: A Practical Tutorial. Evid. Based Ment. Health 2019, 22, 153–160. [Google Scholar] [CrossRef]
- Harrer, M.; Cuijpers, P.; Furukawa, T.; Ebert, D.D. Companion R Package for the Guide Doing Meta-Analysis in R. Available online: https://dmetar.protectlab.org/ (accessed on 18 March 2025).
- Viechtbauer, W. Conducting Meta-Analyses in R with the Metafor Package. J. Stat. Softw. 2010, 36, 1–48. [Google Scholar] [CrossRef]
- Al Assil, R.; Singer, J.; Heidet, M.; Fordyce, C.B.; Scheuermeyer, F.; van Diepen, S.; Sekhon, M.; Leung, K.H.B.; Stenstrom, R.; Christenson, J.; et al. The Association of pH Values during the First 24 h with Neurological Status at Hospital Discharge and Futility among Patients with Out-of-Hospital Cardiac Arrest. Resuscitation 2021, 159, 105–114. [Google Scholar] [CrossRef]
- Cocchi, M.N.; Miller, J.; Hunziker, S.; Carney, E.; Salciccioli, J.; Farris, S.; Joyce, N.; Zimetbaum, P.; Howell, M.D.; Donnino, M.W. The Association of Lactate and Vasopressor Need for Mortality Prediction in Survivors of Cardiac Arrest. Minerva Anestesiol. 2011, 77, 1063–1071. [Google Scholar]
- Dell’Anna, A.M.; Sandroni, C.; Lamanna, I.; Belloni, I.; Donadello, K.; Creteur, J.; Vincent, J.-L.; Taccone, F.S. Prognostic Implications of Blood Lactate Concentrations after Cardiac Arrest: A Retrospective Study. Ann. Intensive Care 2017, 7, 101. [Google Scholar] [CrossRef]
- Düring, J.; Dankiewicz, J.; Cronberg, T.; Hassager, C.; Hovdenes, J.; Kjaergaard, J.; Kuiper, M.; Nielsen, N.; Pellis, T.; Stammet, P.; et al. Lactate, Lactate Clearance and Outcome after Cardiac Arrest: A Post-Hoc Analysis of the TTM-Trial. Acta Anaesthesiol. Scand. 2018, 62, 1436–1442. [Google Scholar] [CrossRef]
- Freire Jorge, P.; Boer, R.; Posma, R.A.; Harms, K.C.; Hiemstra, B.; Bens, B.W.J.; Nijsten, M.W. Early Lactate and Glucose Kinetics Following Return to Spontaneous Circulation after Out-of-Hospital Cardiac Arrest. BMC Res. Notes 2021, 14, 183. [Google Scholar] [CrossRef]
- Han, K.S.; Kim, S.J.; Lee, E.J.; Park, K.Y.; Lee, J.Y.; Lee, S.W. Impact of Rapid Lactate Clearance as an Indicator of Hemodynamic Optimization on Outcome in Out-of-Hospital Cardiac Arrest: A Retrospective Analysis. PLoS ONE 2019, 14, e0214547. [Google Scholar] [CrossRef] [PubMed]
- Hope Kilgannon, J.; Hunter, B.R.; Puskarich, M.A.; Shea, L.; Fuller, B.M.; Jones, C.; Donnino, M.; Kline, J.A.; Jones, A.E.; Shapiro, N.I.; et al. Partial Pressure of Arterial Carbon Dioxide after Resuscitation from Cardiac Arrest and Neurological Outcome: A Prospective Multi-Center Protocol-Directed Cohort Study. Resuscitation 2019, 135, 212–220. [Google Scholar] [CrossRef] [PubMed]
- Hayashida, K.; Suzuki, M.; Yonemoto, N.; Hori, S.; Tamura, T.; Sakurai, A.; Tahara, Y.; Nagao, K.; Yaguchi, A.; Morimura, N.; et al. Early Lactate Clearance Is Associated with Improved Outcomes in Patients with Postcardiac Arrest Syndrome: A Prospective, Multicenter Observational Study (SOS-KANTO 2012 Study). Crit. Care Med. 2017, 45, e559–e566. [Google Scholar] [CrossRef] [PubMed]
- Kiehl, E.L.; Amuthan, R.; Adams, M.P.; Love, T.E.; Enfield, K.B.; Gimple, L.W.; Cantillon, D.J.; Menon, V. Initial Arterial pH as a Predictor of Neurologic Outcome after Out-of-Hospital Cardiac Arrest: A Propensity-Adjusted Analysis. Resuscitation 2019, 139, 76–83. [Google Scholar] [CrossRef]
- Kim, J.C.; Lee, B.K.; Lee, D.H.; Jung, Y.H.; Cho, Y.S.; Lee, S.M.; Lee, S.J.; Park, C.H.; Jeung, K.W. Association between Lactate Clearance during Post-Resuscitation Care and Neurologic Outcome in Cardiac Arrest Survivors Treated with Targeted Temperature Management. Clin. Exp. Emerg. Med. 2017, 4, 10–18. [Google Scholar] [CrossRef]
- Kliegel, A.; Losert, H.; Sterz, F.; Holzer, M.; Zeiner, A.; Havel, C.; Laggner, A.N. Serial Lactate Determinations for Prediction of Outcome after Cardiac Arrest. Medicine 2004, 83, 274–279. [Google Scholar] [CrossRef]
- Laurikkala, J.; Skrifvars, M.B.; Bäcklund, M.; Tiainen, M.; Bendel, S.; Karhu, J.; Varpula, T.; Vaahersalo, J.; Pettilä, V.; Wilkman, E.; et al. Early Lactate Values After Out-of-Hospital Cardiac Arrest: Associations with One-Year Outcome. Shock 2019, 51, 168–173. [Google Scholar] [CrossRef]
- Lee, D.H.; Cho, I.S.; Lee, S.H.; Min, Y.I.; Min, J.H.; Kim, S.H.; Lee, Y.H.; Korean Hypothermia Network Investigators. Correlation between Initial Serum Levels of Lactate After Return of Spontaneous Circulation and Survival and Neurological Outcomes in Patients Who Undergo Therapeutic Hypothermia after Cardiac Arrest. Resuscitation 2015, 88, 143–149. [Google Scholar] [CrossRef]
- Lin, C.-H.; Yu, S.-H.; Chen, C.-Y.; Huang, F.-W.; Chen, W.-K.; Shih, H.-M. Early Blood pH as an Independent Predictor of Neurological Outcome in Patients with Out-of-Hospital Cardiac Arrest. Medicine 2021, 100, e25724. [Google Scholar] [CrossRef]
- Lonsain, W.S.; De Lausnay, L.; Wauters, L.; Desruelles, D.; Dewolf, P. The Prognostic Value of Early Lactate Clearance for Survival after Out-of-Hospital Cardiac Arrest. Am. J. Emerg. Med. 2021, 46, 56–62. [Google Scholar] [CrossRef]
- Orban, J.-C.; Novain, M.; Cattet, F.; Plattier, R.; Nefzaoui, M.; Hyvernat, H.; Raguin, O.; Kaidomar, M.; Kerever, S.; Ichai, C. Association of Serum Lactate with Outcome after Out-of-Hospital Cardiac Arrest Treated with Therapeutic Hypothermia. PLoS ONE 2017, 12, e0173239. [Google Scholar] [CrossRef]
- Park, J.H.; Wee, J.H.; Choi, S.P.; Oh, J.H.; Cheol, S. Assessment of Serum Biomarkers and Coagulation/Fibrinolysis Markers for Prediction of Neurological Outcomes of out of Cardiac Arrest Patients Treated with Therapeutic Hypothermia. Clin. Exp. Emerg. Med. 2019, 6, 9–18. [Google Scholar] [CrossRef] [PubMed]
- Peluso, L.; Belloni, I.; Calabró, L.; Dell’Anna, A.M.; Nobile, L.; Creteur, J.; Vincent, J.-L.; Taccone, F.S. Oxygen and Carbon Dioxide Levels in Patients after Cardiac Arrest. Resuscitation 2020, 150, 1–7. [Google Scholar] [CrossRef] [PubMed]
- Rezar, R.; Lichtenauer, M.; Schwaiger, P.; Seelmaier, C.; Pretsch, I.; Ausserwinkler, M.; Reichle, J.; Jirak, P.; Jung, C.; Strohmer, B.; et al. Thinking Fast and Slow: Lactate and MELD-XI (Model for End-Stage Liver Disease Excluding INR) Are Useful for Estimating Mortality after Cardiopulmonary Resuscitation. Minerva Anestesiol. 2021, 87, 1017–1024. [Google Scholar] [CrossRef]
- Rosenberg, R.D.; Guo, C.-Y.C.; Chatterjee, S.; Schreyer, K.E.; Bashir, R.; O’Murchu, B.; Aggarwal, V.; DeAngelis, M.; Edmundowicz, D.; O’Neill, B.P. The Prognostic Value of Initial Serum Lactate for Survival in Postcardiac Arrest Patients Undergoing Cardiac Catheterization. Catheter. Cardiovasc. Interv. 2021, 97, 228–234. [Google Scholar] [CrossRef]
- Ryoo, S.M.; Kim, Y.-J.; Sohn, C.H.; Ahn, S.; Seo, D.W.; Kim, W.Y. Prognostic Abilities of Serial Neuron-Specific Enolase and Lactate and Their Combination in Cardiac Arrest Survivors During Targeted Temperature Management. J. Clin. Med. 2020, 9, 159. [Google Scholar] [CrossRef]
- Sarıaydın, T.; Çorbacıoğlu, Ş.K.; Çevik, Y.; Emektar, E. Effect of Initial Lactate Level on Short-Term Survival in Patients with out-of-Hospital Cardiac Arrest. Turk. J. Emerg. Med. 2017, 17, 123–127. [Google Scholar] [CrossRef]
- Sauter, T.C.; Iten, N.; Schwab, P.R.; Hautz, W.E.; Ricklin, M.E.; Exadaktylos, A.K. Out-of-Hospital Cardiac Arrests in Switzerland: Predictors for Emergency Department Mortality in Patients with ROSC or on-Going CPR on Admission to the Emergency Department. PLoS ONE 2017, 12, e0188180. [Google Scholar] [CrossRef]
- Seeger, F.H.; Toenne, M.; Lehmann, R.; Ehrlich, J.R. Simplistic Approach to Prognosis after Cardiopulmonary Resuscitation-Value of pH and Lactate. J. Crit. Care 2013, 28, 317.e13–317.e20. [Google Scholar] [CrossRef]
- Shin, J.; Lim, Y.S.; Kim, K.; Lee, H.J.; Lee, S.J.; Jung, E.; You, K.M.; Yang, H.J.; Kim, J.J.; Kim, J.; et al. Initial Blood pH during Cardiopulmonary Resuscitation in Out-of-Hospital Cardiac Arrest Patients: A Multicenter Observational Registry-Based Study. Crit. Care 2017, 21, 322. [Google Scholar] [CrossRef]
- Shinozaki, K.; Oda, S.; Sadahiro, T.; Nakamura, M.; Hirayama, Y.; Watanabe, E.; Tateishi, Y.; Nakanishi, K.; Kitamura, N.; Sato, Y.; et al. Blood Ammonia and Lactate Levels on Hospital Arrival as a Predictive Biomarker in Patients with Out-of-Hospital Cardiac Arrest. Resuscitation 2011, 82, 404–409. [Google Scholar] [CrossRef] [PubMed]
- Sivaraju, A.; Gilmore, E.J.; Wira, C.R.; Stevens, A.; Rampal, N.; Moeller, J.J.; Greer, D.M.; Hirsch, L.J.; Gaspard, N. Prognostication of Post-Cardiac Arrest Coma: Early Clinical and Electroencephalographic Predictors of Outcome. Intensive Care Med. 2015, 41, 1264–1272. [Google Scholar] [CrossRef] [PubMed]
- Starodub, R.; Abella, B.S.; Grossestreuer, A.V.; Shofer, F.S.; Perman, S.M.; Leary, M.; Gaieski, D.F. Association of Serum Lactate and Survival Outcomes in Patients Undergoing Therapeutic Hypothermia after Cardiac Arrest. Resuscitation 2013, 84, 1078–1082. [Google Scholar] [CrossRef] [PubMed]
- Takaki, S.; Kamiya, Y.; Tahara, Y.; Tou, M.; Shimoyama, A.; Iwashita, M. Blood pH Is a Useful Indicator for Initiation of Therapeutic Hypothermia in the Early Phase of Resuscitation after Comatose Cardiac Arrest: A Retrospective Study. J. Emerg. Med. 2013, 45, 57–64. [Google Scholar] [CrossRef]
- Tetsuhara, K.; Kato, H.; Kanemura, T.; Okada, I.; Kiriu, N. Severe Acidemia on Arrival Not Predictive of Neurologic Outcomes in Post-Cardiac Arrest Patients. Am. J. Emerg. Med. 2016, 34, 425–428. [Google Scholar] [CrossRef]
- Isenschmid, C.; Kalt, J.; Gamp, M.; Tondorf, T.; Becker, C.; Tisljar, K.; Locher, S.; Schuetz, P.; Marsch, S.; Hunziker, S. Routine Blood Markers from Different Biological Pathways Improve Early Risk Stratification in Cardiac Arrest Patients: Results from the Prospective, Observational COMMUNICATE Study. Resuscitation 2018, 130, 138–145. [Google Scholar] [CrossRef]
- Tolins, M.L.; Henning, D.J.; Gaieski, D.F.; Grossestreuer, A.V.; Jaworski, A.; Johnson, N.J. Initial Arterial Carbon Dioxide Tension Is Associated with Neurological Outcome after Resuscitation from Cardiac Arrest. Resuscitation 2017, 114, 53–58. [Google Scholar] [CrossRef]
- Von Auenmueller, K.I.; Christ, M.; Sasko, B.M.; Trappe, H.-J. The Value of Arterial Blood Gas Parameters for Prediction of Mortality in Survivors of Out-of-Hospital Cardiac Arrest. J. Emerg. Trauma Shock 2017, 10, 134–139. [Google Scholar] [CrossRef]
- Williams, T.A.; Martin, R.; Celenza, A.; Bremner, A.; Fatovich, D.; Krause, J.; Arena, S.; Finn, J. Use of Serum Lactate Levels to Predict Survival for Patients with Out-of-Hospital Cardiac Arrest: A Cohort Study. Emerg. Med. Australas. 2016, 28, 171–178. [Google Scholar] [CrossRef]
- Yanagawa, Y.; Sakamoto, T.; Sato, H. Relationship between Laboratory Findings and the Outcome of Cardiopulmonary Arrest. Am. J. Emerg. Med. 2009, 27, 308–312. [Google Scholar] [CrossRef]
- Zhang, M.; Zhang, Q.; Yu, Y.; An, L.; Qi, Z.; Li, C. Effects of Early Hemodynamics, Oxygen Metabolism, and Lactate Dynamics on Prognosis of Post-Cardiac Arrest Syndrome. Chin. Med. J. 2022, 135, 344–346. [Google Scholar] [CrossRef] [PubMed]
- Dadeh, A.; Nuanjaroan, B. Using Initial Serum Lactate Level in the Emergency Department to Predict the Sustained Return of Spontaneous Circulation in Nontraumatic Out-of-Hospital Cardiac Arrest Patients. Open Access Emerg. Med. 2018, 10, 105–111. [Google Scholar] [CrossRef] [PubMed]
- Soloperto, R.; Magni, F.; Farinella, A.; Bogossian, E.G.; Peluso, L.; De Luca, N.; Taccone, F.S.; Annoni, F. A Comparison of Prognostic Factors in a Large Cohort of In-Hospital and Out-of-Hospital Cardiac Arrest Patients. Life 2024, 14, 403. [Google Scholar] [CrossRef] [PubMed]
- Kim, S.; Yang, H.; Rhee, B.; Song, H.; Kim, H. Predicting Survival Outcomes in Post-Cardiac Arrest Syndrome: The Impact of Combined Sequential Organ Failure Assessment Score and Serum Lactate Measurement. Med. Sci. Monit. 2023, 29, e942119-1–e942119-9. [Google Scholar] [CrossRef]
- Kandilcik, M.; Arslan, M.; Öksüz, H.; Gişi, G.; Yavuz, C.; Öksüz, G.; Doğaner, A. Evaluation of the Factors Affecting Mortality after Cardiac Arrest—Do Lactate and Procalcitonin Concentrations Have Any Implications? Eur. Rev. Med. Pharmacol. Sci. 2024, 28, 3430–3438. [Google Scholar] [CrossRef]
- Imamura, S.; Miyata, M.; Tagata, K.; Yokomine, T.; Ohmure, K.; Kawasoe, M.; Otsuji, H.; Chaen, H.; Oketani, N.; Ogawa, M.; et al. Prognostic Predictors in Patients with Cardiopulmonary Arrest: A Novel Equation for Evaluating the 30-Day Mortality. J. Cardiol. 2023, 82, 146–152. [Google Scholar] [CrossRef]
- Dusik, M.; Rob, D.; Smalcova, J.; Havranek, S.; Karasek, J.; Smid, O.; Brodska, H.L.; Kavalkova, P.; Huptych, M.; Bakker, J.; et al. Serum Lactate in Refractory Out-of-Hospital Cardiac Arrest: Post-Hoc Analysis of the Prague OHCA Study. Resuscitation 2023, 192, 109935. [Google Scholar] [CrossRef]
- Choi, S.Y.; Oh, S.H.; Park, K.N.; Youn, C.S.; Kim, H.J.; Park, S.H.; Lim, J.Y.; Kim, H.J.; Bang, H.J. Association between Early Lactate-Related Variables and 6-Month Neurological Outcome in out-of-Hospital Cardiac Arrest Patients. Am. J. Emerg. Med. 2024, 78, 62–68. [Google Scholar] [CrossRef]
- Chen, D.-L.; Chung, C.-M.; Wang, G.-J.; Chang, K.-C. Lactate-to-Albumin Ratio and Cholesterol Levels Predict Neurological Outcome in Cardiac Arrest Survivors. Am. J. Emerg. Med. 2024, 83, 9–15. [Google Scholar] [CrossRef]
- Mueller, M.; Jankow, E.; Grafeneder, J.; Schoergenhofer, C.; Poppe, M.; Schriefl, C.; Clodi, C.; Koch, M.; Ettl, F.; Holzer, M.; et al. The Difference between Arterial pCO2 and etCO2 after Cardiac Arrest—Outcome Predictor or Marker of Unfavorable Resuscitation Circumstances? Am. J. Emerg. Med. 2022, 61, 120–126. [Google Scholar] [CrossRef]
- Riveiro, D.F.M.; de Oliveira, V.M.; Braunner, J.S.; Vieira, S.R.R. Evaluation of Serum Lactate, Central Venous Saturation, and Venous-Arterial Carbon Dioxide Difference in the Prediction of Mortality in Postcardiac Arrest Syndrome. J. Intensive Care Med. 2016, 31, 544–552. [Google Scholar] [CrossRef] [PubMed]
- Sun, H.; Xu, Y.; Yue, X.; Gao, Q. Serum pH and Lactate Predict Outcomes after Cardiopulmonary Resuscitation. Acta Medica Mediterr. 2020, 36, 801–804. [Google Scholar]
- George, S.; Thomas, M.; Ibrahim, W.H.; Abdussalam, A.; Chandra, P.; Ali, H.S.; Raza, T. Somatic Survival and Organ Donation among Brain-Dead Patients in the State of Qatar. BMC Neurol. 2016, 16, 207. [Google Scholar] [CrossRef]
Exposures and Outcomes | Studies Reporting Results Based on Cutoffs | Results Based on Population-Level Statistics | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Study | Years of Inclusion | OHCA vs. IHCA | Study Design | Exposure (pH or Lactate) | Outcome | Sample Size | Data Type | Cutoff | Exposure < Cutoff | Exposure > Cutoff | Median (IQR) or Mean (SD) * | Other Results (e.g., OR, HR, AUC, etc.) | p-Value | ||
(n, median [IQR], mean [SD], OR) | |||||||||||||||
n | % | n | % | ||||||||||||
Al Assil et al., 2021 [30] | 2011–2015 | OHCA | Retrospective cohort | pH | mRS 0–3 at discharge | 1345 | n | 7.08 | 131 | 9.7% | 1214 | 90.3% | <0.001 | ||
pH | mRS 4–6 at discharge | 2844 | n | 7.08 | 880 | 30.9% | 1964 | 69.1% | <0.001 | ||||||
pH | mRS 0–3 at discharge | 1345 | n | 7.21 | 397 | 29.5% | 948 | 70.5% | <0.001 | ||||||
pH | mRS 4–6 at discharge | 2844 | n | 7.21 | 1617 | 56.9% | 1227 | 43.1% | <0.001 | ||||||
pH | mRS 0–3 at discharge | 1345 | n | 7.3 | 892 | 66.3% | 453 | 33.7% | <0.001 | ||||||
pH | mRS 4–6 at discharge | 2844 | n | 7.3 | 2240 | 78.8% | 604 | 21.2% | <0.001 | ||||||
Carr et al., 2020 [18] | 2016–2018 | OHCA | Retrospective cohort | pH | Survival to discharge | 13 | n | 7.2 | 6 | 46.2% | 7 | 53.8% | <0.001 | ||
pH | Died prior to discharge | 66 | n | 7.2 | 61 | 92.4% | 5 | 7.6% | <0.001 | ||||||
pH | Survival to discharge | 13 | Median (IQR) | 7.21 (7.04–7.28) | <0.001 | ||||||||||
pH | Died prior to discharge | 66 | Median (IQR) | 6.90 (6.82–7.02) | <0.001 | ||||||||||
Lactate | Survival to discharge | 13 | Mean (SD) | 9 (6.1) * | 0.03 | ||||||||||
Lactate | Died prior to discharge | 66 | Mean (SD) | 12.4 (4.7) * | 0.03 | ||||||||||
pH | Survival to discharge | 79 | OR | 7.2 | 0.08 (0.02–0.32) | <0.001 | |||||||||
Chen et al., 2024 [73] | 2015–2023 | OHCA | Retrospective cohort | Lactate | CPC 1–2 at 30 days | 87 | Mean (SD) | 10.3 (5.4) * | <0.001 | ||||||
Lactate | CPC 3–5 at 30 days | 132 | Mean (SD) | 13.0 (6.0) * | <0.001 | ||||||||||
pH | CPC 1–2 at 30 days | 87 | Mean (SD) | 7.13 (0.18) * | 0.01 | ||||||||||
pH | CPC 3–5 at 30 days | 132 | Mean (SD) | 7.05 (0.20) * | 0.01 | ||||||||||
Choi et al., 2024 [72] | 2009–2022 | OHCA | Retrospective cohort | Lactate | CPC 1–2 at 6 months | 122 | Median (IQR) | 6.85 (3.60–11.35) | <0.001 | ||||||
Lactate | CPC 3–5 at 6 months | 225 | Median (IQR) | 10.90 (7.05–14.70) | <0.001 | ||||||||||
Cocchi et al., 2020 [19] | 2008–2016 | OHCA | Prospective observational cohort | Lactate | Survival to discharge | 96 | n | 5 | 54 | 56.3% | 42 | 43.8% | <0.001 | ||
Lactate | Died prior to discharge | 152 | n | 5 | 45 | 29.6% | 107 | 70.4% | <0.001 | ||||||
Lactate | Survival to discharge | 96 | n | 10 | 88 | 91.7% | 8 | 8.3% | <0.001 | ||||||
Lactate | Died prior to discharge | 132 | n | 10 | 93 | 70.5% | 39 | 29.5% | <0.001 | ||||||
Lactate | Survival to discharge | 97 | Median (IQR) | 4.1 (2.8–6.9) | <0.001 | ||||||||||
Lactate | Died prior to discharge | 152 | Median (IQR) | 6.8 (4.2–10.1) | <0.001 | ||||||||||
Lactate | Died prior to discharge | 99 | OR | <5 | 1 (reference range) | ||||||||||
Lactate | Died prior to discharge | 103 | OR | 5–10 | 2.33 (1.32–4.1) | 0.004 | |||||||||
Lactate | Died prior to discharge | 47 | OR | >10 | 5.85 (2.48–13.8 | <0.001 | |||||||||
Cocchi et al., 2011 [31] | 2006–2008 (A) and 2006–2007 (B) | OHCA | Retrospective cohort | Lactate | Survival to discharge | 37 | n | 5 | 22 | 59.5% | 15 | 40.5% | <0.0001 | ||
Lactate | Died prior to discharge | 91 | n | 5 | 14 | 15.4% | 77 | 84.6% | <0.0001 | ||||||
Lactate | Survival to discharge | 37 | n | 10 | 32 | 86.5% | 5 | 13.5% | <0.0001 | ||||||
Lactate | Died prior to discharge | 91 | n | 10 | 34 | 37.4% | 57 | 62.6% | <0.0001 | ||||||
Lactate | Died prior to discharge | 36 | OR | <5 | 1 (reference range) | ||||||||||
Lactate | Died prior to discharge | 30 | OR | 5–10 | 3.1 (1.1–8.7) | 0.03 | |||||||||
Lactate | Died prior to discharge | 62 | OR | >10 | 17.9 (5.8–55.6) | <0.0001 | |||||||||
Dadeh et al., 2018 [66] | 2015–2017 | OHCA | Retrospective cohort | Lactate | Survival to discharge | 85 | Mean (SD) | 12 (4.8) * | 0.381 | ||||||
Lactate | Died prior to discharge | 122 | Mean (SD) | 12.6 (5) * | 0.381 | ||||||||||
Dell’Anna et al., 2017 [32] | 2009–2013 | Combined (58% OHCA) | Prospective observational cohort | Lactate | CPC 1–2 at three months | 74 | Median (IQR) | 2.5 (1.5–5.5) | <0.001 | ||||||
Lactate | CPC 3–5 at three months | 162 | Median (IQR) | 5.3 (2.9–9.0) | <0.001 | ||||||||||
pH | CPC 1–2 at three months | 74 | Median (IQR) | 7.27 (7.18–7.37) | 0.09 | ||||||||||
pH | CPC 3–5 at three months | 162 | Median (IQR) | 7.25 (7.15–7.34) | 0.09 | ||||||||||
Donnino et al., 2014 [20] | 2011–2012 | OHCA | Retrospective cohort | pH | Survival to discharge | 46 | Median (IQR) | 7.3 (7.2–7.3) | 0.02 | ||||||
pH | Died prior to discharge | 54 | Median (IQR) | 7.2 (7.1–7.3) | 0.02 | ||||||||||
Lactate | Survival to discharge | 46 | Median (IQR) | 4.1 (2.6–7.7) | 0.004 | ||||||||||
Lactate | Died prior to discharge | 54 | Median (IQR) | 7.3 (3.4–10.9) | 0.004 | ||||||||||
Lactate | mRS 0–3 at discharge | 30 | Median (IQR) | 3.9 (2.7–6.1) | 0.009 | ||||||||||
Lactate | mRS 4–6 at discharge | 70 | Median (IQR) | 7.0 (3.0–10.4) | 0.009 | ||||||||||
Lactate | AUC for predicting mortality | AUC | 0.67 | ||||||||||||
Lactate | AUC for predicting mRS 4–6 at discharge | AUC | 0.67 | ||||||||||||
During et al., 2018 [33] | 2010–2013 | OHCA | Retrospective cohort | Lactate | Survival to 30 days | 486 | n | 6 | 297 | 61.1% | 189 | 38.9% | <0.001 | ||
Lactate | Died prior to 30 days | 391 | n | 6 | 147 | 37.6% | 244 | 62.4% | <0.001 | ||||||
Lactate | CPC 1–2 at 180 days | 408 | n | 6 | 250 | 61.3% | 158 | 38.7% | <0.001 | ||||||
Lactate | CPC 3–5 at 180 days | 469 | n | 6 | 194 | 41.4% | 275 | 58.6% | <0.001 | ||||||
Lactate | Died prior to 30 days | 391 | Median (IQR) | 7.30 (4.5–10.7) | |||||||||||
Lactate | Survival to 30 days | 486 | Median (IQR) | 4.65 (2.4–8) | |||||||||||
Dusik et al., 2023 [71] | 2013–2020 | OHCA | Prospective randomized clinical trial | Lactate | CPC 1–2 from 30–180 days | 48 | Median (IQR) | 7.8 (5.7–10.2) | 0.055 | ||||||
Lactate | CPC 3–5 from 30–180 days | 34 | Median (IQR) | 9.7 (7.1–12.1) | 0.055 | ||||||||||
Freire-Jorge et al., 2021 [34] | 2006–2016 | OHCA | Retrospective cohort | Lactate | Survival to discharge | 82 | Mean (95% CI) | 12.4 (11.4–12.9) | 0.951 | ||||||
Lactate | Died prior to discharge | 73 | Mean (95% CI) | 12.2 (11.2–13.1) | 0.951 | ||||||||||
Han et al., 2019 [35] | 2006–2017 | OHCA | Prospective observational cohort | Lactate | Survival to discharge | 145 | Mean (SD) | 9.2 (4.0) * | <0.001 | ||||||
Lactate | Died prior to discharge | 190 | Mean (SD) | 11.9 (5.1) * | <0.001 | ||||||||||
Hope Kilgannon et al., 2019 [36] | 2013–2017 | Combined (77% OHCA) | Prospective observational cohort | pH | mRS 0–3 at discharge | 85 | Mean (95% CI) | 7.28 (7.22–7.36) * | 0.007 | ||||||
pH | mRS 4–6 at discharge | 195 | Mean (95% CI) | 7.26 (7.17–7.33) * | 0.007 | ||||||||||
Imamura et al., 2023 [70] | 2015–2022 | OHCA | Retrospective cohort | pH | mRS 0–3 at 30 days | 78 | Mean (SD) | 7.14 (0.21) * | <0.001 | ||||||
pH | mRS 4–6 at 30 days | 116 | Mean (SD) | 6.95 (0.17) * | <0.001 | ||||||||||
lactate | mRS 0–3 at 30 days | 78 | Mean (SD) | 8.74 (4.92) * | <0.001 | ||||||||||
lactate | mRS 4–6 at 30 days | 116 | Mean (SD) | 12.57 (4.85) * | <0.001 | ||||||||||
Kandilcik et al, 2024 [69] | 2017–2020 | Combined (70% OHCA) | Retrospective cohort | pH | Survival to hospital discharge | 36 | Mean (SD) | 7.32 (0.11) * | 0.028 | ||||||
pH | Died prior to hospital discharge | 115 | Mean (SD) | 7.26 (0.15) * | 0.028 | ||||||||||
Lactate | Survival to hospital discharge | 36 | Median (Min-Max) | 1.9 (0.6–16) | <0.001 | ||||||||||
Lactate | Died prior to hospital discharge | 115 | Median (Min-Max) | 5.1 (0.7–20) | <0.001 | ||||||||||
Kei et al., 2017 [37] | 2012–2013 | OHCA | Prospective observational cohort | Lactate | Survival to 30 days | 256 | Mean (SD) | 8.3 (4.5) * | <0.001 | ||||||
Lactate | Died prior to 30 days | 287 | Mean (SD) | 11.3 (5.3) * | <0.001 | ||||||||||
Kiehl et al., 2019 [38] | 2008–2014 (UVA) and 2012 to 2017 (CCF) | OHCA | Prospective observational cohort | Lactate | CPC 1–2 at discharge | 142 | Mean (SD) | 6.0 (3.3) * | <0.001 | ||||||
Lactate | CPC 3–5 at discharge | 581 | Mean (SD) | 8.6 (4.4) * | <0.001 | ||||||||||
pH | CPC 1–2 at discharge | 142 | Mean (SD) | 7.23 (0.11) * | <0.001 | ||||||||||
pH | CPC 3–5 at discharge | 581 | Mean (SD) | 7.11 (0.18) * | <0.001 | ||||||||||
Kim et al., 2023 [68] | 2016–2020 | OHCA | Retrospective cohort | Lactate | Hospital discharge | 236 | Mean (SD) | 7.24 (4.42) * | <0.001 | ||||||
Lactate | Hospital discharge | 202 | Mean (SD) | 10.32 (4.83) * | <0.001 | ||||||||||
Kim et al., 2017 [39] | 2012–2015 | Combined (81% OHCA) | Retrospective cohort | Lactate | CPC 3–5 at discharge | 282 | OR (95% CI) | 1.049 (0.962–1.143) | |||||||
Lactate | Died prior to discharge | 282 | OR (95% CI) | 1.063 (0.981–1.152) | |||||||||||
Kliegel et al., 2004 [40] | 1991–2001 | OHCA | Retrospective cohort | Lactate | Survival to 6 months | 194 | Median (IQR) | 7.8 (5.4–10.8) | <0.01 | ||||||
Lactate | Died prior to 6 months | 200 | Median (IQR) | 9 (6.5–11.9) | <0.01 | ||||||||||
Lactate | CPC 1–2 at 6 months | 186 | Median (IQR) | 7.6 (5.4–10.3) | <0.001 | ||||||||||
Lactate | CPC 3–5 at 6 months | 208 | Median (IQR) | 9.2 (6.7–12.1) | <0.001 | ||||||||||
Laurikkala et al., 2019 [41] | 2010–2011 | OHCA | Prospective observational cohort | Lactate | CPC 1–2 at 1 year | 185 | Median (IQR) | 3.06 (2.68–3.44) | <0.001 | ||||||
Lactate | CPC 3–5 at 1 year | 273 | Median (IQR) | 4.76 (4.29–5.23) | <0.001 | ||||||||||
Lee et al., 2015 [42] | 2007–2012 | OHCA | Retrospective cohort | Lactate | Survival to discharge | 254 | Mean (SD) | 9.55 (4.33) * | Authors state “statistically significant, no p value given | ||||||
Lactate | Died prior to discharge | 289 | Mean (SD) | 11.36 (4.58) * | Authors state “statistically significant, no p value given | ||||||||||
Lactate | CPC 1–2 at discharge | 96 | Mean (SD) | 8.99 (4.17) * | Not specified | ||||||||||
Lactate | CPC 3–5 at discharge | 347 | Mean (SD) | 10.70 (4.55) * | Not specified | ||||||||||
Lin et al., 2021 [43] | 2012–2018 | OHCA | Retrospective cohort | pH | CPC 1–2 at discharge | 70 | Mean (SD) | 7.13 (0.19) * | <0.001 | ||||||
pH | CPC 3–5 at discharge | 1964 | Mean (SD) | 6.98 (0.17) * | <0.001 | ||||||||||
pH | CPC 3–5 at discharge | 1964 | OR (univariate) | 0.02 (0.01–0.05) | <0.001 | ||||||||||
pH | CPC 3–5 at discharge | 1964 | OR (multivariate) | 0.03 (0.01–0.13) | <0.001 | ||||||||||
pH | CPC 1–2 at discharge | 2034 | AUC | 0.7316 | |||||||||||
Lonsain et al., 2021 [44] | 2012–2019 | OHCA | Retrospective cohort | Lactate | Survival to 48 h | 152 | Median (IQR) | 8.2 (0.9–22) | no p value reported | ||||||
Lactate | Died prior to 48 h | 24 | Median (IQR) | 11.3 (3.7–29.0) | no p value reported | ||||||||||
Lactate | Survival to 72 h | 131 | Median (IQR) | 7.5 (0.9–22.0) | no p value reported | ||||||||||
Lactate | Died prior to 72 h | 61 | Median (IQR) | 10.1 (5.0–18.0) | no p value reported | ||||||||||
Marinšek et al., 2020 [9] | 2014–2016 | OHCA | Retrospective cohort | Lactate | CPC 1–2 at 72 h | 41 | n | 6 | 38 | 92.7% | 3 | 7.3% | <0.001 | ||
Lactate | CPC 3–5 at 72 h | 69 | n | 6 | 39 | 56.5% | 30 | 43.5% | <0.001 | ||||||
Momiyama et al., 2017 [21] | 2010–2013 | OHCA | Retrospective cohort | Lactate | CPC 1–2 at discharge | 31 | Mean (SD) | 9.1 (5.4) * | “not significant” | ||||||
Lactate | CPC 3–5 at discharge | 341 | Mean (SD) | 10.7 (4.6) * | “not significant” | ||||||||||
pH | CPC 1–2 at discharge | 31 | Mean (SD) | 7.26 (0.16) * | <0.001 | ||||||||||
pH | CPC 3–5 at discharge | 341 | Mean (SD) | 6.93 (0.19) * | <0.001 | ||||||||||
Orban et al., 2017 [45] | 2006–2013 | OHCA | Retrospective cohort | Lactate | CPC 1–2 at discharge | 89 | Median (IQR) | 5.4 (3.3–9.4) | < 0.01 | ||||||
Lactate | CPC 3–5 at discharge | 183 | Median (IQR) | 2.2 (1.5–3.6) | < 0.01 | ||||||||||
Lactate | CPC 3–5 at discharge | 272 | OR (95% CI) | Lactate > 4 | 7.54 (4.07–14.0) | <0.0001 | |||||||||
Park et al., 2019 [46] | 2011–2016 | OHCA | Retrospective cohort | Lactate | CPC 1–2 at discharge | 39 | Median (IQR) | 3.98 (2.00–9.04) | 0.492 | ||||||
Lactate | CPC 3–5 at discharge | 63 | Median (IQR) | 4.96 (3.20–9.43) | 0.492 | ||||||||||
Peluso et al., 2020 [47] | 2009–2017 | Combined (65% OHCA) | Retrospective cohort | Lactate | Survival to discharge | 147 | Median (IQR) | 4.3 (3.0–7.1) | <0.05 | ||||||
Lactate | Died prior to discharge | 209 | Median (IQR) | 7.0 (4.6–9.2) | <0.05 | ||||||||||
Lactate | CPC 1–2 at 3 months | 126 | Median (IQR) | 4.2 (2.8–6.4) | <0.05 | ||||||||||
Lactate | CPC 3–5 at 3 months | 230 | Median (IQR) | 6.8 (4.6–9.3) | <0.05 | ||||||||||
Lactate | CPC 3–5 at 3 months | 356 | OR | 1.16 (1.06–1.29) | 0.002 | ||||||||||
Lactate | Died prior to discharge | 356 | OR | 1.11 (1.02–1.19) | 0.017 | ||||||||||
Rezar et al., 2021 [48] | 2018–2020 | Combined (84% OHCA) | Retrospective cohort | Lactate | Died prior to 30 days | 106 | HR | 1.15 (1.07–1.24) | <0.001 | ||||||
Lactate | Died prior to 30 days | 106 | AUC | 0.75 (0.66–0.83) | 0.11 | ||||||||||
Lactate | Survival to 30 days | 72 | n | 2.5 | 46 | 63.9% | 26 | 36.1% | |||||||
Lactate | Died prior to 30 days | 32 | n | 2.5 | 5 | 15.6% | 27 | 84.4% | |||||||
Rosenberg et al., 2021 [49] | 2014–2018 | Combined (76% OHCA) | Retrospective cohort | Lactate | Survival to discharge | 20 | Mean (SD) | 4.7 (3.8)* | <0.01 | ||||||
Lactate | Died prior to discharge | 30 | Mean (SD) | 9.8 (4.7)* | <0.01 | ||||||||||
Lactate | Died prior to discharge | 50 | OR | 1.39 (1.13–1.71) | <0.01 | ||||||||||
Lactate | Died prior to discharge | 50 | aOR | 1.56 (1.19–2.05) | <0.01 | ||||||||||
Lactate | Survival to discharge | 20 | n | 4.5 | 12 | 60.0% | 8 | 40.0% | <0.05 | ||||||
Lactate | Died prior to discharge | 30 | n | 4.5 | 4 | 13.3% | 26 | 86.7% | <0.05 | ||||||
Lactate | Survival to discharge | 20 | n | 9 | 17 | 85.0% | 3 | 15.0% | <0.05 | ||||||
Lactate | Died prior to discharge | 30 | n | 9 | 16 | 53.3% | 14 | 46.7% | <0.05 | ||||||
Ryoo et al., 2020 [50] | 2013–2018 | OHCA | Retrospective cohort | Lactate | CPC 3–5 at 28 days | 98 | Median (IQR) | 10.3 (7.1–13.5) | <0.05 | ||||||
Lactate | CPC 1–2 at 28 days | 62 | Median (IQR) | 7.5 (4.1–10.2) | <0.05 | ||||||||||
Sarıaydın et al., 2017 [51] | 2015–2016 | OHCA | Prospective observational cohort | Lactate | Survival to 24 h | 42 | Mean (SD) | 8.67 (2.94) * | 0.1 | ||||||
Lactate | Died prior to 24 h | 98 | Mean (SD) | 10 (3.1) * | 0.1 | ||||||||||
pH | Survival to 24 h | 42 | Mean (SD) | 7.02 (0.2) * | 0.7 | ||||||||||
pH | Died prior to 24 h | 98 | Mean (SD) | 6.96 (0.17) * | 0.7 | ||||||||||
Sauter et al., 2017 [52] | 2012–2015 | OHCA | Retrospective cohort | pH | Survival to hospital admission | 215 | Mean (SD) | 7.2 (0.17) * | 0.01 | ||||||
pH | Died prior to hospital admission | 13 | Mean (SD) | 7.01 (0.13) * | 0.01 | ||||||||||
Lactate | Survival to hospital admission | 215 | Mean (SD) | 6.1 (4.4) * | 0.013* | ||||||||||
Lactate | Died prior to hospital admission | 13 | Mean (SD) | 11.2 (5.1) * | 0.01 | ||||||||||
Seeger et al., 2013 [53] | 2007–2009 (retrospective) and 2009–2010 (prospective) | Combined (65% OHCA) | Retrospective Cohort and Prospective observational cohort | Lactate | Combination of death or severe hypoxic brain damage within 30 days | 206 | univariate HR (95CI) | Lactate> 6.94 | 2.772 (1.953–3.936) | <0.001 | |||||
pH | Combination of death or severe hypoxic brain damage within 30 days | 206 | univariate HR (95CI) | pH < 7.21 | 2.706 (1.912–3.830) | <0.001 | |||||||||
Lactate | Combination of death or severe hypoxic brain damage within 30 days | 206 | multivariate HR | Lactate> 6.94 | 2.026 (1.371–2.994) | <0.001 | |||||||||
pH | Combination of death or severe hypoxic brain damage within 30 days | 206 | multivariate HR | pH < 7.21 | 2.027 (1.342–3.060) | 0.001 | |||||||||
Shin et al., 2017 [54] | 2009–2014 | OHCA | Retrospective cohort | pH | Survival to discharge | 311 | Mean (IQR) | 7.00 (6.93–7.31) | <0.001 | ||||||
pH | Died prior to discharge | 1918 | Mean (IQR) | 6.96 (6.83–7.20) | <0.001 | ||||||||||
pH | CPC 1–2 at 28 days | 98 | Mean (IQR) | 7.11 (7.00–7.26) | <0.001 | ||||||||||
pH | CPC 3–5 at 28 days | 2131 | Mean (IQR) | 6.96 (6.84–7.09) | <0.001 | ||||||||||
Lactate | Survival to discharge | 311 | Mean (IQR) | 9.5 (6.9–11.7) | 0.006 | ||||||||||
Lactate | Died prior to discharge | 1918 | Mean (IQR) | 10.1 (7.1–13.6) | 0.006 | ||||||||||
Lactate | CPC 1–2 at 28 days | 98 | Mean (IQR) | 8.7 (6.8–10.8) | 0.011 | ||||||||||
Lactate | CPC 3–5 at 28 days | 2131 | Mean (IQR) | 10.1 (7.1–13.4) | 0.011 | ||||||||||
Shinozaki et al., 2011 [55] | 2007–2009 | OHCA | Prospective observational cohort | Lactate | CPC 1–2 at 6 months | 10 | Median (IQR) | 9.2 (2.6–11.5) | <0.05 | ||||||
Lactate | CPC 3–5 at 6 months | 88 | Median (IQR) | 12.1 (9.5–14) | <0.05 | ||||||||||
Lactate | CPC 1–2 at 6 months | 98 | AUC | Lactate < 12 | 0.735 (95% CI: 0.574–0.896), sensitivity 90%, specificity 52.3% | ||||||||||
Lactate | CPC 1–2 at 6 months | 98 | OR | Lactate < 12 | 9.86 (95% CI 1.20–81.1) | ||||||||||
Sivaraju et al., 2015 [56] | 2011–2014 | Combined (97% OHCA) | Prospective observational cohort | pH | GOS 4–5 at discharge | 29 | Median (IQR) | 7.31 (7.25–7.38) | <0.001 | ||||||
pH | GOS 1–3 at discharge | 71 | Median (IQR) | 7.17 (7.08–7.29) | <0.001 | ||||||||||
Lactate | GOS 4–5 at discharge | 29 | Median (IQR) | 3.3 (2.0–5.3) | 0.003 | ||||||||||
Lactate | GOS 1–3 at discharge | 71 | Median (IQR) | 6.0 (3.5–10.9) | 0.003 | ||||||||||
Soloperto et al., 2024 [67] | 2004–2022 | OHCA | Retrospective cohort | Lactate | CPC 1–2 at 3 months | 160 | Median (IQR) | 4.5 (3.6–7.9) | <0.0001 | ||||||
Lactate | CPC 3–5 at 3 months | 407 | Median (IQR) | 7.1 (3.9–10.9) | <0.0001 | ||||||||||
Starodub et al., 2013 [57] | 2005–2011 | Combined (76% OHCA) | Retrospective cohort | Lactate | Survival to discharge | 66 | n | 5 | 15 | 22.7% | 51 | 77.3% | “NS” | ||
Lactate | Died prior to discharge | 88 | n | 5 | 20 | 22.7% | 68 | 77.3% | “NS” | ||||||
Lactate | Survival to discharge | 66 | n | 10 | 42 | 63.6% | 24 | 36.4% | “NS” | ||||||
Lactate | Died prior to discharge | 88 | n | 10 | 52 | 59.1% | 36 | 40.9% | “NS” | ||||||
Takaki et al., 2013 [58] | 2003–2009 | OHCA | Retrospective cohort | Lactate | CPC 1–2 at 6 months | 25 | Mean (Range) | 8.7 [3.2–17.2] | 0.019 | ||||||
Lactate | CPC 3–5 at 6 months | 25 | Mean (Range) | 11.4 [5.8–21.9] | 0.019 | ||||||||||
pH | CPC 1–2 at 6 months | 25 | Mean (Range) | 7.17 [6.894–7.4] | <0.01 | ||||||||||
pH | CPC 3–5 at 6 months | 25 | Mean (Range) | 6.866 [6.666–7.092] | <0.01 | ||||||||||
pH | CPC 1–2 at 6 months | 50 | ROC cutoff | 6.968 | AUC 0.934 (95CI 0.887–0.982), sensitivity 0.92, specificity 0.84 | 0.01 | |||||||||
Tetsuhara et al., 2016 [59] | 2013–2015 | OHCA | Retrospective case-control | pH | CPC 1–2 at discharge | 13 | Median (IQR) | 7.22 (6.87–7.32) | 0.759 | ||||||
pH | CPC 3–5 at discharge | 19 | Median (IQR) | 7.12 (6.92–7.30) | 0.759 | ||||||||||
Lactate | CPC 1–2 at discharge | 13 | Median (IQR) | 6.6 (4.9–9.9) | 0.388 | ||||||||||
Lactate | CPC 3–5 at discharge | 19 | Median (IQR) | 5.9 (3.5–8.9) | 0.388 | ||||||||||
pH | CPC 1–2 at discharge | 13 | n | 7.2 | 6 | 46.2% | 7 | 53.8% | no p value reported | ||||||
pH | CPC 3–5 at discharge | 19 | n | 7.2 | 12 | 63.2% | 7 | 36.8% | no p value reported | ||||||
pH | CPC 1–2 at discharge | 32 | OR (95% CI) | OR 0.5; 95% CI: 0.09–2.61 | 0.47 | ||||||||||
Tisljar et al., 2018 [60] | 2012–2017 | OHCA | Prospective observational cohort | Lactate | Survival to discharge | 165 | Median (IQR) | 2.3 (1.4, 3.5) | <0.001 | ||||||
Lactate | Died prior to discharge | 156 | Median (IQR) | 4.15 (2.345, 7.3) | <0.001 | ||||||||||
Lactate | Died prior to discharge | 321 | OR (95% CI) | OR (9.67; 95% CI:4.60 to 20.33) | <0.001 | ||||||||||
Lactate | Died prior to discharge | 321 | AUC | 0.70 95% CI: (0.65 to 0.76) | |||||||||||
Lactate | CPC at discharge | 321 | OR (95% CI) | OR (12.58; 95% CI: 5.74 to 27.59) | <0.001 | ||||||||||
Lactate | CPC at discharge | 321 | AUC | 0.72 95% CI: (0.66 to 0.78) | |||||||||||
Tolins et al., 2017 [61] | 2005–2011 | Combined (75% OHCA) | Retrospective cohort | pH | CPC 1–2 at discharge | 33 | Mean (95% CI) | 7.27 (7.22–7.31) | no p value reported | ||||||
pH | CPC 3–5 at discharge | 81 | Mean (95% CI) | 7.18 (7.14–7.22) | no p value reported | ||||||||||
vonAuenmueller et al., 2017 [62] | 2008–2013 | OHCA | Retrospective cohort | pH | Died prior to 5 days | 170 | OR (95% CI) | pH < 7.0 | 7.20 (95% CI: 3.11–16.69) | <0.001 | |||||
Lactate | Died prior to 5 days | 170 | OR (95% CI) | Lactate > 5 | 6.79 (95% CI: 2.77–16.66) | <0.001 | |||||||||
pH | Survival to 5 days | 80 | n | 7 | 8 | 10.0% | 72 | 90.0% | no p value reported | ||||||
pH | Died prior to 5 days | 90 | n | 7 | 40 | 44.4% | 50 | 55.6% | no p value reported | ||||||
Lactate | Survival to 5 days | 79 | n | 5 | 29 | 36.7% | 50 | 63.3% | no p value reported | ||||||
Lactate | Died prior to 5 days | 89 | n | 5 | 7 | 7.9% | 82 | 92.1% | no p value reported | ||||||
Williams et al., 2016 [63] | 2007–2012 | OHCA | Retrospective cohort | Lactate | Survival to discharge | 126 | Mean (SD) | 6.9 (4.7)* | <0.001 | ||||||
Lactate | Died prior to discharge | 392 | Mean (SD) | 12.2 (5.5)* | <0.001 | ||||||||||
Lactate | Survival to discharge | 126 | Median (IQR) | 5.9 (4.2–8.9) | <0.001 | ||||||||||
Lactate | Died prior to discharge | 392 | Median (IQR) | 11.1 (8.1–15.0) | <0.001 | ||||||||||
Lactate | CPC 1–2 at discharge | 518 | OR (95% CI) | 0.84 (95% CI 0.77–0.91) | <0.001 | ||||||||||
Yanagawa et al., 2009 [64] | 2005–2007 | OHCA | Retrospective cohort | pH | CPC 1–2 at one month | 16 | Mean (SD) | 7.259 (0.036) * | <0.001 | ||||||
pH | CPC 3–5 at one month | 102 | Mean (SD) | 7.029 (0.018) * | <0.001 | ||||||||||
Zhang et al., 2021 [65] | 2012–2019 | OHCA | Retrospective cohort | Lactate | Survival to 28 days | 476 | Median (IQR) | 3.50 [1.80–7.61] | <0.001 | ||||||
Lactate | Died prior to 28 days | 674 | Median (IQR) | 5.3 [2.27–10.00] | <0.001 | ||||||||||
pH | Survival to 28 days | 476 | Median (IQR) | 7.31 (7.17–7.40) | <0.001 | ||||||||||
pH | Died prior to 28 days | 674 | Median (IQR) | 7.25 (7.09–7.37) | <0.001 |
Favorable | Unfavorable | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Exposure | Outcome | Number of Studies | N | G [95% CI] | I2 [95% CI] | Cochran’s Q (p) | τ2 [95% CI] | Number of Studies | N | G [95% CI] | I2 [95% CI] | Cochran’s Q (p) | τ2 [95% CI] |
Lactate | Survival | 10 | 1279 | 7.24 [6.05, 8.44] | 88.4% [80.8%, 93.0%] | 77.86 (<0.0001) | 2.11 [0.81, 9.89] | 9 | 2698 | 10.11 [8.98, 11.25] | 86.1% [75.6%, 92.1%] | 57.60 (<0.0001) | 1.76 [0.58, 7.67] |
Lactate | Neurologic Outcome | 13 | 1005 | 7.15 [6.37, 7.93] | 86.5% [78.6%, 91.5%] | 88.96 (<0.0001) | 1.31 [0.46, 4.01] | 10 | 1738 | 8.76 [7.45, 10.07] | 92.2% [87.7%, 95.0%] | 114.69 (<0.0001) | 2.88 [1.20, 11.19] |
pH | Survival | 6 | 828 | 7.22 [7.10, 7.33] | 95.8% [93.0%, 97.4%] | 117.77 (<0.0001) | 0.01 [0.004, 0.07] | 6 | 1103 | 7.16 [7.03, 7.29] | 98.5% [97.9%, 99.0%] | 335.23 (<0.0001) | 0.02 [0.006, 0.09] |
pH | Neurologic Outcome | 9 | 482 | 7.22 [7.17, 7.27] | 85.4% [74.1%, 91.7%] | 54.72 (<0.001) | 0.003 [0.0009, 0.013] | 7 | 1011 | 7.09 [7.00, 7.18] | 97.4% [96.1%, 98.2%] | 230.59 (<0.0001) | 0.007 [0.002, 0.05] |
Study (Author, Year) | Selection | Comparability | Outcome | Overall |
---|---|---|---|---|
Al Assil et al., 2021 [30] | Low | High | Low | Low |
Carr et al., 2020 [18] | Low | Low | Low | Low |
Chen et al., 2024 [73] | Low | Some concern | Low | Low |
Choi et al., 2024 [72] | Low | High | Low | Low |
Cocchi et al., 2020 [19] | Low | Some concern | Low | Low |
Cocchi et al., 2011 [31] | Low | Some concern | Low | Low |
Dell’Anna et al., 2017 [32] | Some concern | Some concern | Low | Low |
Dadeh et al., 2018 [66] | Low | Some concern | Low | Low |
Donnino et al., 2014 [20] | Low | Some concern | Low | Low |
During et al., 2018 [33] | Low | Low | Low | Low |
Dusik et al., 2023 [71] | Low | Some concern | Low | Low |
FreireJorge et al., 2021 [34] | Low | Some concern | Low | Low |
Han et al., 2019 [35] | Low | Some concern | Low | Low |
HopeKilgannon et al., 2019 [36] | Some concern | High | Low | Medium |
Imamura et al., 2023 [70] | Low | Some concern | Low | Low |
Kandilcik et al, 2024 [69] | Some concern | Some concern | Low | Low |
Kei et al., 2017 [37] | Low | High | Some concern | Medium |
Kiehl et al., 2019 [38] | Low | High | Low | Low |
Kim et al., 2023 [68] | Low | Some concern | Low | Low |
Kim et al., 2017 [39] | Some concern | High | Low | Medium |
Kliegel et al., 2004 [40] | Low | High | Some concern | Medium |
Laurikkala et al., 2019 [41] | Low | High | Low | Low |
Lee et al., 2015 [42] | Low | High | Some concern | Medium |
Lin et al., 2021 [43] | Low | High | Low | Low |
Lonsain et al., 2021 [44] | Low | High | Low | Low |
Marinšek et al., 2020 [9] | Low | High | Low | Low |
Momiyama et al., 2017 [21] | Low | High | Low | Low |
Orban et al., 2017 [45] | Low | High | Low | Low |
Park et al., 2019 [46] | Low | Low | Low | Low |
Peluso et al., 2020 [47] | Some concern | Some concern | Low | Low |
Rezar et al., 2021 [48] | Some concern | High | Low | Medium |
Rosenberg et al., 2021 [49] | Some concern | Low | Low | Low |
Ryoo et al., 2020 [50] | Low | High | Low | Low |
Sarıaydın et al., 2017 [51] | Low | Some concern | Low | Low |
Sauter et al., 2017 [52] | Low | High | Low | Low |
Seeger et al., 2013 [53] | Some concern | High | Some concern | Medium |
Shin et al., 2017 [54] | Low | High | Low | Low |
Shinozaki et al., 2011 [55] | Low | Some concern | Low | Low |
Sivaraju et al., 2015 [56] | Some concern | Some concern | Low | Low |
Soloperto et al., 2024 [67] | Low | High | Low | Low |
Starodub et al., 2013 [57] | Some concern | High | Low | Medium |
Takaki et al., 2013 [58] | Low | Some concern | Low | Low |
Tetsuhara et al., 2016 [59] | Low | Some concern | Low | Low |
Tisljar et al., 2018 [60] | Low | High | Low | Low |
Tolins et al., 2017 [61] | Some concern | Some concern | Low | Low |
vonAuenmueller et al., 2017 [62] | Low | High | Low | Low |
Williams et al., 2016 [63] | Low | High | Low | Low |
Yanagawa et al., 2009 [64] | Low | Some concern | Low | Low |
Zhang et al., 2021 [65] | Low | High | Low | Low |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Patel, N.T.; Carr, C.T.; Hopson, C.M.; Hwang, C.W. Lactate and pH as Independent Biomarkers for Prognosticating Meaningful Post-out-of-Hospital Cardiac Arrest Outcomes: A Systematic Review and Meta-Analysis. J. Clin. Med. 2025, 14, 2244. https://doi.org/10.3390/jcm14072244
Patel NT, Carr CT, Hopson CM, Hwang CW. Lactate and pH as Independent Biomarkers for Prognosticating Meaningful Post-out-of-Hospital Cardiac Arrest Outcomes: A Systematic Review and Meta-Analysis. Journal of Clinical Medicine. 2025; 14(7):2244. https://doi.org/10.3390/jcm14072244
Chicago/Turabian StylePatel, Nishil T., Casey T. Carr, Charlotte M. Hopson, and Charles W. Hwang. 2025. "Lactate and pH as Independent Biomarkers for Prognosticating Meaningful Post-out-of-Hospital Cardiac Arrest Outcomes: A Systematic Review and Meta-Analysis" Journal of Clinical Medicine 14, no. 7: 2244. https://doi.org/10.3390/jcm14072244
APA StylePatel, N. T., Carr, C. T., Hopson, C. M., & Hwang, C. W. (2025). Lactate and pH as Independent Biomarkers for Prognosticating Meaningful Post-out-of-Hospital Cardiac Arrest Outcomes: A Systematic Review and Meta-Analysis. Journal of Clinical Medicine, 14(7), 2244. https://doi.org/10.3390/jcm14072244