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The LAS VEGAS risk score for prediction of postoperative pulmonary complications: An observational study
Open accessAbstract
BACKGROUND: Currently used pre-operative prediction scores for postoperative pulmonary complications (PPCs) use patient data and expected surgery characteristics exclusively. However, intra-operative events are also associated with the development of PPCs. OBJECTIVE: We aimed to develop a new prediction score for PPCs that uses both pre-operative and intra-operative data. DESIGN: This is a secondary analysis of the LAS VEGAS study, a large international, multicentre, prospective study. SETTINGS: A total of 146 hospitals across 29 countries. PATIENTS: Adult patients requiring intra-operative ventilation during general anaesthesia for surgery. INTERVENTIONS: The cohort was randomly divided into a development subsample to construct a predictive model, and a subsample for validation. MAIN OUTCOME MEASURES: Prediction performance of developed models for PPCs. RESULTS: Of the 6063 patients analysed, 10.9% developed at least one PPC. Regression modelling identified 13 independent risk factors for PPCs: six patient characteristics [higher age, higher American Society of Anesthesiology (ASA) physical score, pre-operative anaemia, pre-operative lower SpO2 and a history of active cancer or obstructive sleep apnoea], two procedure-related features (urgent or emergency surgery and surgery lasting ≥ 1 h), and five intra-operative events [use of an airway other than a supraglottic device, the use of intravenous anaesthetic agents along with volatile agents (balanced anaesthesia), intra-operative desaturation, higher levels of positive end-expiratory pressures > 3 cmH2O and use of vasopressors]. The area under the receiver operating characteristic curve of the LAS VEGAS risk score for prediction of PPCs was 0.78 [95% confidence interval (95% CI), 0.76 to 0.80] for the development subsample and 0.72 (95% CI, 0.69 to 0.76) for the validation subsample. CONCLUSION: The LAS VEGAS risk score including 13 peri-operative characteristics has a moderate discriminative ability for prediction of PPCs. External validation is needed before use in clinical practice. TRIAL REGISTRATION: The study was registered at Clinicaltrials.gov, number NCT01601223.
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Before the SARS-CoV-2 pandemic, high-quality multinational observational studies established overall baseline rates of postoperative pulmonary complications up to 10% and subsequent mortality up to 3% after surgery.
“Before the SARS-CoV-2 pandemic, high-quality, multinational observational studies established overall baseline rates of postoperative pulmonary complications (up to 10%) and subsequent mortality (up to 3%) after surgery.5, 6, 7”
From Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study (2020) · cites this paper as [5]
In the POPULAR multicentre prospective observational study of 211 hospitals from 28 European countries in 2014-15, the pulmonary complication rate was 8%.
“in the POPULAR multicentre, prospective, observational study of 211 hospitals from 28 European countries in 2014–15, the pulmonary complication rate was 8%.5”
From Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study (2020) · cites this paper as [5]
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Citations
Cites
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649
Collins, P. G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · British Journal of Cancer · doi:10.1038/bjc.2014.639 · Cited by 3,315
Alba, A. C., Agoritsas, T., Walsh, M., Hanna, S., Iorio, A., Devereaux, P. J., McGinn, T., & Guyatt, G. (2017) · JAMA · doi:10.1001/jama.2017.12126 · Cited by 1,631
Prediction of Postoperative Pulmonary Complications in a Population-based Surgical Cohort
Canet, J., Gallart, L., Gomar, C. G., Paluzié, G., Vallés, J., Castillo, J., Sabaté, S., Mazo, V., Briones, Z., & Sanchis, J. (2010) · Anesthesiology · doi:10.1097/aln.0b013e3181fc6e0a · Cited by 1,463
Self-citationMortality after surgery in Europe: a 7 day cohort study
Pearse, R. M., Moreno, R. P., Bauer, P., Pelosi, P. P., Metnitz, P., Spies, C., Vallet, B., Vincent, J., Hoeft, A., & Rhodes, A. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61148-9 · Cited by 1,354
Self-citationSurgical Risk Factors, Morbidity, and Mortality in Elderly Patients
Turrentine, F. E., Wang, H., Simpson, V. B., & Jones, R. S. (2006) · Journal of the American College of Surgeons · doi:10.1016/j.jamcollsurg.2006.08.026 · Cited by 1,062
Smetana, G. W., Lawrence, V. A., & Cornell, J. E. (2006) · Annals of Internal Medicine · doi:10.7326/0003-4819-144-8-200604180-00009 · Cited by 1,040
Hemmes, S. N. T., de Abreu, M. G., Pelosi, P. P., & Schultz, M. J. (2014) · The Lancet · doi:10.1016/s0140-6736(14)60416-5 · Cited by 714
Self-citationSurgical risk factors, morbidity, and mortality in elderly patients
Turrentine, F. E., Wang, H., Simpson, V. B., & Jones, R. S. (2008) · Critical Reviews in Oncology/Hematology · doi:10.1016/s1040-8428(08)70016-7 · Cited by 711
How to develop a more accurate risk prediction model when there are few events
Pavlou, M., Ambler, G., Seaman, S. R., Guttmann, O. P., Elliot, P. M., King, M., & Omar, R. (2015) · BMJ · doi:10.1136/bmj.h3868 · Cited by 698
Gupta, R. M., Parvizi, J., Hanssen, A. D., & Gay, P. C. (2001) · Mayo Clinic Proceedings · doi:10.4065/76.9.897 · Cited by 622
Prospective External Validation of a Predictive Score for Postoperative Pulmonary Complications
Mazo, V., Sabaté, S., Canet, J., Gallart, L., de Abreu, M. G., Belda, J., Langeron, O., Hoeft, A., & Pelosi, P. P. (2014) · Anesthesiology · doi:10.1097/aln.0000000000000334 · Cited by 453
Self-citationClinical review: Positive end-expiratory pressure and cardiac output.
Luecke, T., & Pelosi, P. P. (2005) · Critical Care · doi:10.1186/cc3877 · Cited by 445
Self-citationProtective versus Conventional Ventilation for Surgery
Neto, A. S., Hemmes, S. N. T., Barbas, C. S. V., Beiderlinden, M., Biehl, M., Binnekade, J. M., Canet, J., Fernandez-Bustamante, A., Futier, E., Gajic, O., et al. (2015) · Anesthesiology · doi:10.1097/aln.0000000000000706 · Cited by 353
Self-citationIncidence of and Risk Factors for Pulmonary Complications after Nonthoracic Surgery
McAlister, F. A., Bertsch, K., Man, J., Bradley, J., & Jacka, M. J. (2004) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200408-1069oc · Cited by 346
Collins, P. G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2014.11.010 · Cited by 336
Ladha, K. S., Melo, M. F. V., McLean, D. J., Wanderer, J. P., Grabitz, S. D., Kurth, T., & Eikermann, M. (2015) · BMJ · doi:10.1136/bmj.h3646 · Cited by 331
Henderson, A. R. (2005) · Clinica Chimica Acta · doi:10.1016/j.cccn.2005.04.002 · Cited by 328
The LAS VEGAS investigators (2017) · European Journal of Anaesthesiology · doi:10.1097/eja.0000000000000646 · Cited by 321
Neto, A. S., Hemmes, S. N. T., Barbas, C. S. V., Beiderlinden, M., Fernandez-Bustamante, A., Futier, E., Hollmann, M. W., Jaber, S., Kozian, A., Licker, M. J., et al. (2014) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(14)70228-0 · Cited by 314
Self-citationDevelopment and Validation of a Score for Prediction of Postoperative Respiratory Complications
Brueckmann, B., Villa‐Uribe, J. L., Bateman, B. T., Grosse-Sundrup, M., HESS, D. R., Schlett, C. L., & Eikermann, M. (2013) · Anesthesiology · doi:10.1097/aln.0b013e318293065c · Cited by 271
Derivation and Diagnostic Accuracy of the Surgical Lung Injury Prediction Model
Kor, D. J., Warner, D. O., Alsara, A., Pérez, E. R. F., Malinchoc, M., Kashyap, R., Li, G., & Gajic, O. (2011) · Anesthesiology · doi:10.1097/aln.0b013e31821b5839 · Cited by 172
Silva, J. M., de Oliveira, A. M. R., Nogueira, F. A. M., Vianna, P. M. M., Filho, M. C. P., Dias, L. F., Maia, V. P., Neucamp, C. D. S., Amêndola, C. P., Carmona, M. J. C., et al. (2013) · Critical Care · doi:10.1186/cc13151 · Cited by 146
Anaesthetic management of patients with sleep apnoea syndrome
Boushra, N. N. (1996) · Canadian Journal of Anesthesia/Journal canadien d anesthésie · doi:10.1007/bf03011774 · Cited by 116
Predicting Risk of Postoperative Lung Injury in High-risk Surgical Patients
Kor, D. J., Lingineni, R. K., Gajic, O., Park, P. K., Blum, J. M., Hou, P. C., Hoth, J. J., Anderson, H. L., Bajwa, E. K., Bartz, R. R., et al. (2014) · Anesthesiology · doi:10.1097/aln.0000000000000216 · Cited by 108
Role of the Venous Return in Critical Illness and Shock
Funk, D. J., Jacobsohn, E., & Kumar, A. (2012) · Critical Care Medicine · doi:10.1097/ccm.0b013e31827bfc25 · Cited by 103
Jong, M. A. C. D., Ladha, K. S., Melo, M. F. V., Staehr-Rye, A. K., Bittner, E. A., Kurth, T., & Eikermann, M. (2015) · Annals of Surgery · doi:10.1097/sla.0000000000001499 · Cited by 81
Berry, A. J., Smith, R. B., Weintraub, W. S., et al. (2001) · Journal of Vascular Surgery · doi:10.1067/mva.2001.111737 · Cited by 65
Ramachandran, S. K., & Kumar, A. M. (2014) · Respiratory Care · doi:10.4187/respcare.02976 · Cited by 54
Utsumi, M., Shimizu, J., Miyamoto, A., et al. (2010) · Journal of Hospital Infection · doi:10.1016/j.jhin.2010.01.021 · Cited by 38
Miyakita, H., Sadahiro, S., Saito, G., Okada, K., Tanaka, A., & Suzuki, T. (2016) · International Journal of Clinical Oncology · doi:10.1007/s10147-016-1054-1 · Cited by 35
ESA Clinical Trials Network 2012
Hemmes, S. N. T., de Abreu, M. G., Pelosi, P. P., & Schultz, M. J. (2013) · European Journal of Anaesthesiology · doi:10.1097/eja.0b013e32835fcab3 · Cited by 31
Self-citationA score to estimate 30-day mortality after intensive care admission after cardiac surgery
Lamarche, Y., Elmi-Sarabi, M., Ding, L., Abel, J. G., Sirounis, D., & Denault, A. Y. (2016) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2016.11.039 · Cited by 29
Durga, P., Sahu, B. P., Mantha, S., et al. (2009) · Anesthesia & Analgesia · doi:10.1213/ane.0b013e3181c293a9 · Cited by 24
Mazo, V., Sabaté, S., & Canet, J. (2013) · Anesthesiology · doi:10.1097/aln.0b013e31829ce927 · Cited by 20
Self-citationRiss, S., Mittlböck, M., Riss, K. C., et al. (2014) · International Journal of Surgery · doi:10.1016/j.ijsu.2014.07.003 · Cited by 19
Küpeli̇, E., Dedekargınoğlu, B. E., Ulubay, G., et al. (2017) · Experimental and Clinical Transplantation · doi:10.6002/ect.mesot2016.p89 · Cited by 15
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Metadata
- Authors
- Neto, A. S., da Costa, L. G. V., Hemmes, S. N. T., Canet, J., Hedenstierna, G., Jaber, S., Hiesmayr, M., Hollmann, M. W., Mills, G. H., Vidal Melo, M. F., Pearse, R., Putensen, C., Schmid, W., Severgnini, P., Wrigge, H., Gama de Abreu, M., Pelosi, P., & Schultz, M. J.
- Affiliations
- University of Bonn (DE)
- Corresponding author
- Neto, Ary Serpa
- ORCID
- Venue
- European Journal of Anaesthesiology
- Published
- 2018-09
- Volume
- 35
- Issue
- 9
- Pages
- 691-701
- ISSN
- 0265-0215 (ISSN-L)
- PMC
- PMC7450515
- OpenAlex
- W2809430993
- Cited by
- 160
- References
- 37
- Citations by year
- 2026: 13 · 2025: 24 · 2024: 24 · 2023: 17 · 2022: 30 · 2021: 20 · 2020: 20 · 2019: 8 · 2018: 3
- FWCI
- 9.63
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- European Journal of Anaesthesiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- HAL (submittedVersion, other-oa)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- IrInSubria (University of Insubria) (submittedVersion)
- CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion)
- Archivio Istituzionale della Ricerca - Università Vita-Salute San Raffaele (submittedVersion)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion, other-oa)
- Funders
- Massachusetts General Hospital
- Queen Mary University of London
- Royal Devon and Exeter NHS Foundation Trust
- University Hospitals Plymouth NHS Trust
- National Institute for Health and Care Research (RP_2014-04-022)
- Universiteit van Amsterdam
- Vrije Universiteit Amsterdam
- Newcastle upon Tyne Hospitals NHS Foundation Trust
- Università degli Studi di Genova
- Università degli Studi dell'Insubria
- Sociedade Beneficente Israelita Brasileira Albert Einstein
- Haukeland Universitetssjukehus
- Miljø- og Fødevareministeriet
- European Society of Anaesthesiology
- Lietuvos Sveikatos Mokslų Universitetas
- Barts Health NHS Trust
- National Institutes of Health
- National Heart, Lung, and Blood Institute (R01HL121228, R34HL123438)
- NIH Clinical Center
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac, Anesthesia and Surgical Outcomes
- Health Sciences › Medicine › Anesthesiology and Pain Medicine › Airway Management and Intubation Techniques
- Health Sciences › Medicine › Anesthesiology and Pain Medicine › Anesthesia and Pain Management
- Keywords
- postoperative pulmonary complications, risk scoring, general anesthesia, risk factors, positive end-expiratory pressure, vasopressors, supraglottic airway devices, balanced anesthesia, preoperative anemia, obstructive sleep apnea, emergency surgery
- MeSH terms
- Age Factors, Female, Health Status, Humans, Intraoperative Period, Lung, Lung Diseases, Male, Middle Aged, Postoperative Complications, Predictive Value of Tests, Prospective Studies, Risk Factors, Reproducibility of Results, Cohort Studies, Preoperative Period, Operative Time
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