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Stahlschmidt A, Passos SC, Cardoso GR, Schuh GJ, Neto PCDS, Castro SMDJ, Stefani LC. Postoperative intensive care allocation and mortality in high-risk surgical patients: evidence from a low- and middle-income country cohort. BRAZILIAN JOURNAL OF ANESTHESIOLOGY (ELSEVIER) 2024; 74:844517. [PMID: 38789003 DOI: 10.1016/j.bjane.2024.844517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/10/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024]
Abstract
BACKGROUND The escalation of surgeries for high-risk patients in Low- and Middle-Income Countries (LMICs) lacks evidence on the positive impact of Intensive Care Unit (ICU) admission and lacks universal criteria for allocation. This study explores the link between postoperative ICU allocation and mortality in high-risk patients within a LMIC. Additionally, it assesses the Ex-Care risk model's utility in guiding postoperative allocation decisions. METHODS A secondary analysis was conducted in a cohort of high-risk surgical patients from a 800-bed university-affiliated teaching hospital in Southern Brazil (July 2017 to January 2020). Inclusion criteria encompassed 1431 inpatients with Ex-Care Model-assessed all-cause postoperative 30-day mortality risk exceeding 5%. The study compared 30-day mortality outcomes between those allocated to the ICU and the Postanesthetic Care Unit (PACU). Outcomes were also assessed based on Ex-Care risk model classes. RESULTS Among 1431 high-risk patients, 250 (17.47%) were directed to the ICU, resulting in 28% in-hospital 30-day mortality, compared to 8.9% in the PACU. However, ICU allocation showed no independent effect on mortality (RR = 0.91; 95% CI 0.68‒1.20). Patients in the highest Ex-Care risk class (Class IV) exhibited a substantial association with mortality (RR = 2.11; 95% CI 1.54-2.90) and were more frequently admitted to the ICU (23.3% vs. 13.1%). CONCLUSION Patients in the highest Ex-Care risk class and those with complications faced elevated mortality risk, irrespective of allocation. Addressing the unmet need for adaptable postoperative care for high-risk patients outside the ICU is crucial in LMICs. Further research is essential to refine criteria and elucidate the utility of risk assessment tools like the Ex-Care model in assisting allocation decisions.
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Affiliation(s)
- Adriene Stahlschmidt
- Universidade Federal do Rio Grande do Sul (UFRGS), Faculdade de Medicina, Programa de Pós-Graduação em Ciências Médicas, Porto Alegre, RS, Brazil; Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil.
| | - Sávio Cavalcante Passos
- Universidade Federal do Rio Grande do Sul (UFRGS), Faculdade de Medicina, Programa de Pós-Graduação em Ciências Médicas, Porto Alegre, RS, Brazil; Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil.
| | | | | | - Paulo Corrêa da Silva Neto
- Universidade Federal do Rio Grande do Sul (UFRGS), Faculdade de Medicina, Programa de Pós-Graduação em Ciências Médicas, Porto Alegre, RS, Brazil.
| | | | - Luciana Cadore Stefani
- Universidade Federal do Rio Grande do Sul (UFRGS), Faculdade de Medicina, Departamento de Cirurgia, Porto Alegre, RS, Brazil.
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Passos SC, de Jezus Castro SM, Stahlschmidt A, da Silva Neto PC, Irigon Pereira PJ, da Cunha Leal P, Lopes MB, Dos Reis Falcão LF, de Azevedo VLF, Lineburger EB, Mendes FF, Vilela RM, de Araújo Azi LMT, Antunes FD, Braz LG, Stefani LC. Development and validation of the Ex-Care BR model: a multicentre initiative for identifying Brazilian surgical patients at risk of 30-day in-hospital mortality. Br J Anaesth 2024:S0007-0912(24)00188-0. [PMID: 38729814 DOI: 10.1016/j.bja.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Surgical risk stratification is crucial for enhancing perioperative assistance and allocating resources efficiently. However, existing models may not capture the complexity of surgical care in Brazil. Using data from various healthcare settings nationwide, we developed a new risk model for 30-day in-hospital mortality (the Ex-Care BR model). METHODS A retrospective cohort study was conducted in 10 hospitals from different geographic regions in Brazil. Data were analysed using multilevel logistic regression models. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), Brier score, and calibration plots. Derivation and validation cohorts were randomly assigned. RESULTS A total of 107,372 patients were included, and 30-day in-hospital mortality was 2.1% (n=2261). The final risk model comprised four predictors related to the patient and surgery (age, ASA physical status classification, surgical urgency, and surgical size), and the random effect related to hospitals. The model showed excellent discrimination (AUROC=0.93, 95% confidence interval [CI], 0.93-0.94), calibration, and overall performance (Brier score=0.017) in the derivation cohort (n=75,094). Similar results were observed in the validation cohort (n=32,278) (AUROC=0.93, 95% CI, 0.92-0.93). CONCLUSIONS The Ex-Care BR is the first model to consider regional and organisational peculiarities of the Brazilian surgical scene, in addition to patient and surgical factors. It is particularly useful for identifying high-risk surgical patients in situations demanding efficient allocation of limited resources. However, a thorough exploration of mortality variations among hospitals is essential for a comprehensive understanding of risk. CLINICAL TRIAL REGISTRATION NCT05796024.
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Affiliation(s)
- Sávio C Passos
- Graduate Program in Medical Sciences, School of Medicine, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil; Anesthesiology and Perioperative Medicine Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Stela M de Jezus Castro
- Department of Statistics, Institute of Mathematics and Statistics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Adriene Stahlschmidt
- Anesthesiology and Perioperative Medicine Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Paulo C da Silva Neto
- Graduate Program in Medical Sciences, School of Medicine, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | | | | | | | - Luiz F Dos Reis Falcão
- Department of Surgery, School of Medicine, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | | | | | - Florentino F Mendes
- Department of Surgical Clinic, School of Medicine, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, Brazil
| | - Ramon M Vilela
- Department of Anesthesiology, Irmandade Santa Casa de Misericórdia Porto Alegre, Porto Alegre, Brazil
| | - Liana M T de Araújo Azi
- Department of Anesthesiology and Surgery, School of Medicine, Universidade Federal da Bahia (UFBA), Salvador, Brazil
| | - Fabrício D Antunes
- Department of Medicine, School of Medicine, Universidade Federal de Sergipe (UFS), Aracaju, Brazil
| | - Leandro G Braz
- Department of Surgical Specialties and Anesthesiology, School of Medicine, Universidade Estadual Paulista (UNESP), Botucatu, Brazil
| | - Luciana C Stefani
- Graduate Program in Medical Sciences, School of Medicine, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil; Department of Surgery, School of Medicine, Universidade Federal do Rio Grande do Sul (UFRGS), Hospital de Clínicas de Porto Alegre, Brazil.
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Stahlschmidt A, Passos SC, Dornelles DD, Polanczyk C, Gutierrez CS, Minuzzi RR, Castro SMJ, Stefani LC. Troponin elevation as a marker of short deterioration and one-year death in a high-risk surgical patient cohort in a low and middle income country setting: a postoperative approach to increase surveillance. Can J Anaesth 2023; 70:1776-1788. [PMID: 37853279 DOI: 10.1007/s12630-023-02558-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 04/02/2023] [Accepted: 04/28/2023] [Indexed: 10/20/2023] Open
Abstract
PURPOSE Myocardial injury after noncardiac surgery is common and mostly asymptomatic. The ideal target population that will benefit from routine troponin measurements in low and middle income countries (LMICs) is unclear. This study aims to evaluate the clinical outcomes of a cohort of high-risk surgical patients according to high-sensitivity troponin T (hsTnT) in an LMIC setting. METHODS We conducted a prospective cohort study of 442 high-risk patients undergoing noncardiac surgery at a Brazilian hospital between February 2019 and March 2020. High-sensitivity troponin T levels were measured preoperatively, 24 hr, and 48 hr after surgery and stratified into three groups: normal (< 20 ng·L-1); minor elevation (20-65 ng·L-1); and major elevation (> 65 ng·L-1). We performed survival analysis to determine the association between myocardial injury and one-year mortality. We described medical interventions and evaluated unplanned intensive care unit (ICU) admission and complications using multivariable models. RESULTS Postoperative myocardial injury occurred in 45% of patients. Overall, 30-day mortality was 8%. Thirty-day and one-year mortality were higher in patients with hsTnT ≥ 20 ng·L-1. One-year mortality was 18% in the unaltered troponin group vs 31% and 41% for minor and major elevation groups, respectively. Multivariable analysis of one-year survival showed a hazard ratio (HR) of 1.94 (95% confidence interval [CI], 1.22 to 3.09) for the minor elevation group and a HR of 2.73 (95% CI, 1.67 to 4.45) for the troponin > 65 ng·L-1 group. Patients with altered troponin had more unplanned ICU admissions (13% vs 5%) and more complications (78% vs 48%). CONCLUSION This study supports evidence that hsTnT is an important prognostic marker and a strong predictor of all-cause mortality after surgery. Troponin measurement in high-risk surgical patients could potentially be used as tool to scale-up care in LMIC settings. STUDY REGISTRATION ClinicalTrials.gov (NCT04187664); first submitted 5 December 2019.
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Affiliation(s)
- Adriene Stahlschmidt
- School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Anaesthesia and Perioperative Medicine Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Sávio C Passos
- School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Anaesthesia and Perioperative Medicine Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Debora D Dornelles
- School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Carisi Polanczyk
- Cardiology Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Claudia S Gutierrez
- Anaesthesia and Perioperative Medicine Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Rosangela R Minuzzi
- Anaesthesia and Perioperative Medicine Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Stela M J Castro
- Department of Statistics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Luciana C Stefani
- School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
- Department of Surgery, School of Medicine, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.
- Anaesthesia and Perioperative Medicine Service, Hospital de Clínicas de Porto Alegre (HCPA), Rua Ramiro Barcelos, 2350, Porto Alegre, RS, 90035-903, Brazil.
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Neto PCS, Rodrigues AL, Stahlschmidt A, Helal L, Stefani LC. Developing and validating a machine learning ensemble model to predict postoperative delirium in a cohort of high-risk surgical patients: A secondary cohort analysis. Eur J Anaesthesiol 2023; 40:356-364. [PMID: 36860180 DOI: 10.1097/eja.0000000000001811] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
BACKGROUND Postoperative delirium (POD) has a negative impact on prognosis, length of stay and the burden of care. Although its prediction and identification may improve postoperative care, this need is largely unmet in the Brazilian public health system. OBJECTIVE To develop and validate a machine-learning prediction model and estimate the incidence of delirium. We hypothesised that an ensemble machine-learning prediction model that incorporates predisposing and precipitating features could accurately predict POD. DESIGN A secondary analysis nested in a cohort of high-risk surgical patients. SETTING An 800-bed, quaternary university-affiliated teaching hospital in Southern Brazil. We included patients operated on from September 2015 to February 2020. PATIENTS We recruited 1453 inpatients with an all-cause postoperative 30-day mortality risk greater than 5% assessed preoperatively by the ExCare Model. MAIN OUTCOME MEASURE The incidence of POD classified by the Confusion Assessment Method, up to 7 days postoperatively. Predictive model performance with different feature scenarios were compared with the area under the receiver operating characteristic curve. RESULTS The cumulative incidence of delirium was 117, giving an absolute risk of 8.05/100 patients. We developed multiple machine-learning nested cross-validated ensemble models. We selected features through partial dependence plot analysis and theoretical framework. We treated the class imbalance with undersampling. Different feature scenarios included: 52 preoperative, 60 postoperative and only three features (age, preoperative length of stay and the number of postoperative complications). The mean areas (95% confidence interval) under the curve ranged from 0.61 (0.59 to 0.63) to 0.74 (0.73 to 0.75). CONCLUSION A predictive model composed of three indicative readily available features performed better than those with numerous perioperative features, pointing to its feasibility as a prognostic tool for POD. Further research is required to test the generalisability of this model. TRIAL REGISTRATION Institutional Review Board Registration number 04448018.8.0000.5327 (Brazilian CEP/CONEP System, available in https://plataformabrasil.saude.gov.br/ ).
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Affiliation(s)
- Paulo C S Neto
- From the Programa de Pós-graduação em Medicina: Ciências Médicas, Universidade Federal do Rio Grande do Sul (PCSN), Universidade Federal do Rio Grande do Sul (ALR), Programa de Pós-graduação em Medicina: Ciências Médicas, Universidade Federal do Rio Grande do Sul (AS), Hospital de Clínicas de Porto Alegre and Universidade Federal do Rio Grande do Sul (LH), Programa de Pós-graduação em Medicina: Ciências Médicas, Professor at Surgical Department -Universidade Federal do Rio Grande do Sul and Chief of Teaching Division of Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil (LCS)
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Zhu Y, Bi Y, Liu B, Zhu T. Assessment of prognostic value of preoperative neutrophil-to-lymphocyte ratio for postoperative mortality and morbidity. Front Med (Lausanne) 2023; 10:1102733. [PMID: 36968819 PMCID: PMC10030720 DOI: 10.3389/fmed.2023.1102733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 02/20/2023] [Indexed: 03/11/2023] Open
Abstract
BackgroundThe preoperative elevated neutrophil-to-lymphocyte ratio (NLR) was reported to be associated with poorer outcomes after cancer and cardiovascular surgeries. It is unclear, however, if the predictive value is particular or if it may be applied to other types of surgery. We aimed to assess the prognostic value of preoperative NLR levels for morbidity and mortality after various surgery and determine an optimal threshold for NLR.MethodsWe conducted a cohort analysis on patients receiving surgery at Sichuan University West China Hospital between 2018 and 2020. Multivariable piecewise regression analysis were used to determine the optimal cutoff value of NLR. Subgroup analysis were performed to verify the correlation. Sensitivity analysis was used to explore the effect of different thresholds.ResultsWe obtained data from 136,347 patients. The optimal cutoff of NLR was determined as 3.6 [95% CI (3.0, 4.1)] by piecewise regression method. After multivariable adjustment, preoperative high NLR remained significantly associated with increased in-hospital mortality (aOR, 2.19; 95% CI, 1.90–2.52; p < 0.001) and ICU admission after surgery (aOR, 1.69; 95% CI, 1.59–1.79; p < 0.001). Subgroup analyses confirmed the predictive value of high NLR in multiple surgical subgroups, including general, orthopedic, neurosurgical, and thoracic surgery subgroups, otorhinolaryngology, head and neck surgery, and burn plastic surgery. A NLR threshold of 3.6 gave excellent predictive value, whether employed alone or added in an extended model.ConclusionsIn conclusion, the association of elevated NLR with higher mortality and ICU admission can be extended to a wider range of procedures. NLR threshold of 3.6 could provide good prognostic value for the prognostic model.
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Affiliation(s)
- Yingchao Zhu
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yaodan Bi
- Department of Anesthesiology, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Bin Liu
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- *Correspondence: Bin Liu
| | - Tao Zhu
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Wild H, Stewart BT, LeBoa C, Jewell T, Mehta K, Wren SM. Perioperative Risk Assessment in Humanitarian Settings: A Scoping Review. World J Surg 2023; 47:1092-1113. [PMID: 36631590 DOI: 10.1007/s00268-023-06893-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/25/2022] [Indexed: 01/13/2023]
Abstract
BACKGROUND No validated perioperative risk assessment models currently exist for use in humanitarian settings. To inform the development of a perioperative mortality risk assessment model applicable to humanitarian settings, we conducted a scoping review of the literature to identify reports that described perioperative risk assessment in surgical care in humanitarian settings and LMICs. METHODS We conducted a scoping review of the literature to identify records that described perioperative risk assessment in low-resource or humanitarian settings. Searches were conducted in databases including: PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, Web of Science, World Health Organization Catalog, and Google Scholar. RESULTS Our search identified 1582 records. After title/abstract and full text screening, 50 reports remained eligible for analysis in quantitative and qualitative synthesis. These reports presented data from over 37 countries from public, NGO, and military facilities. Data reporting was highly inconsistent: fewer than half of reports presented the indication for surgery; less than 25% of reports presented data on injury severity or prehospital data. Most elements of perioperative risk models designed for high-resource settings (e.g., vital signs, laboratory data, and medical comorbidities) were unavailable. CONCLUSION At present, no perioperative mortality risk assessment model exists for use in humanitarian settings. Limitations in consistency and quality of data reporting are a primary barrier, however, can be addressed through data-driven identification of several key variables encompassed by a minimum dataset. The development of such a score is a critical step toward improving the quality of care provided to populations affected by conflict and protracted humanitarian crises.
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Affiliation(s)
- Hannah Wild
- Department of Surgery, University of Washington, 1959 NE Pacific St., Seattle, WA, 98195, USA.
| | - Barclay T Stewart
- Department of Surgery, University of Washington, 1959 NE Pacific St., Seattle, WA, 98195, USA
- Global Injury Control Section, Harborview Injury Prevention and Research Center, Seattle, WA, USA
| | - Christopher LeBoa
- Department of Environmental Health Sciences, University of California Berkeley, Berkeley, CA, USA
| | - Teresa Jewell
- Health Science Library, University of Washington, Seattle, WA, USA
| | - Kajal Mehta
- Department of Surgery, University of Washington, 1959 NE Pacific St., Seattle, WA, 98195, USA
| | - Sherry M Wren
- Stanford University School of Medicine, Stanford, CA, USA
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Reilly JR, Wong D, Brown WA, Gabbe BJ, Myles PS. External validation of a surgical mortality risk prediction model for inpatient noncardiac surgery in an Australian private health insurance dataset. ANZ J Surg 2022; 92:2873-2880. [PMID: 35979735 DOI: 10.1111/ans.17946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 05/26/2022] [Accepted: 06/13/2022] [Indexed: 01/07/2023]
Abstract
BACKGROUND We previously conducted a systematic review to identify surgical mortality risk prediction tools suitable for adapting in the Australian context and identified the Surgical Outcome Risk Tool (SORT) as an ideal model. The primary aim was to investigate the external validity of SORT for predicting in-hospital mortality in a large Australian private health insurance dataset. METHODS A cohort study using a prospectively collected Australian private health insurance dataset containing over 2 million deidentified records. External validation was conducted by applying the predictive equation for SORT to the complete case analysis dataset. Model re-estimation (recalibration) was performed by logistic regression. RESULTS The complete case analysis dataset contained 161 277 records. In-hospital mortality was 0.2% (308/161277). The mean estimated risk given by SORT was 0.2% and the median (IQR) was 0.01% (0.003%-0.08%). Discrimination was high (c-statistic 0.96) and calibration was accurate over the range 0%-10%, beyond which mortality was over-predicted but confidence intervals included or closely approached the perfect prediction line. Re-estimation of the equation did not improve over-prediction. Model diagnostics suggested the presence of outliers or highly influential values. CONCLUSION The low perioperative mortality rate suggests the dataset was not representative of the overall Australian surgical population, primarily due to selection bias and classification bias. Our results suggest SORT may significantly under-predict 30-day mortality in this dataset. Given potential differences in perioperative mortality, private health insurance status and hospital setting should be considered as covariables when a locally validated national surgical mortality risk prediction model is developed.
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Affiliation(s)
- Jennifer Richelle Reilly
- Department of Anaesthesiology and Perioperative Medicine, Alfred Health, Melbourne, Victoria, Australia.,Department of Anaesthesiology and Perioperative Medicine, Monash University, Melbourne, Victoria, Australia
| | - Darren Wong
- Department of Gastroenterology, Austin Health, Heidelberg, Victoria, Australia.,Department of Medicine, University of Melbourne, Parkville, Victoria, Australia
| | - Wendy Ann Brown
- Department of Surgery, Alfred Health, Melbourne, Victoria, Australia.,Department of Surgery, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Belinda Jane Gabbe
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Paul Stewart Myles
- Department of Anaesthesiology and Perioperative Medicine, Alfred Health, Melbourne, Victoria, Australia.,Department of Anaesthesiology and Perioperative Medicine, Monash University, Melbourne, Victoria, Australia
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Torlot F, Yew CY, Reilly JR, Phillips M, Weber DG, Corcoran TB, Ho KM, Toner AJ. External validity of four risk scores predicting 30-day mortality after surgery. BJA OPEN 2022; 3:100018. [PMID: 37588588 PMCID: PMC10430818 DOI: 10.1016/j.bjao.2022.100018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 05/23/2022] [Indexed: 08/18/2023]
Abstract
Background Surgical risk prediction tools can facilitate shared decision-making and efficient allocation of perioperative resources. Such tools should be externally validated in target populations before implementation. Methods Predicted risk of 30-day mortality was retrospectively derived for surgical patients at Royal Perth Hospital from 2014 to 2021 using the Surgical Outcome Risk Tool (SORT) and the related NZRISK (n=44 031, 53 395 operations). In a sub-population (n=31 153), the Physiology and Operative Severity Score for the enumeration of Mortality (POSSUM) and the Portsmouth variant of this (P-POSSUM) were matched from the Copeland Risk Adjusted Barometer (C2-Ai, Cambridge, UK). The primary outcome was risk score discrimination of 30-day mortality as evaluated by area-under-receiver operator characteristic curve (AUROC) statistics. Calibration plots and outcomes according to risk decile and time were also explored. Results All four risk scores showed high discrimination (AUROC) for 30-day mortality (SORT=0.922, NZRISK=0.909, P-POSSUM=0.893; POSSUM=0.881) but consistently over-predicted risk. SORT exhibited the best discrimination and calibration. Thresholds to denote the highest and second-highest deciles of SORT risk (>3.92% and 1.52-3.92%) captured the majority of deaths (76% and 13%, respectively) and hospital-acquired complications. Year-on-year SORT calibration performance drifted towards over-prediction, reflecting a decrease in 30-day mortality over time despite an increase in the surgical population risk. Conclusions SORT was the best performing risk score in predicting 30-day mortality after surgery. Categorising patients based on SORT into low, medium (80-90th percentile), and high risk (90-100th percentile) might guide future allocation of perioperative resources. No tools were sufficiently calibrated to support shared decision-making based on absolute predictions of risk.
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Affiliation(s)
| | | | - Jennifer R. Reilly
- Department of Anaesthesiology and Perioperative Medicine, Alfred Hospital, Melbourne, Australia
- Department of Anaesthesia and Perioperative Medicine, Monash University, Melbourne, Australia
| | | | - Dieter G. Weber
- Royal Perth Hospital, Perth, Australia
- University of Western Australia, Perth, Australia
| | - Tomas B. Corcoran
- Royal Perth Hospital, Perth, Australia
- University of Western Australia, Perth, Australia
| | - Kwok M. Ho
- Royal Perth Hospital, Perth, Australia
- University of Western Australia, Perth, Australia
| | - Andrew J. Toner
- Royal Perth Hospital, Perth, Australia
- University of Western Australia, Perth, Australia
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Schmidt AP, Stefani LC. How to identify a high-risk surgical patient? BRAZILIAN JOURNAL OF ANESTHESIOLOGY (ENGLISH EDITION) 2022; 72:313-315. [PMID: 35461896 PMCID: PMC9373624 DOI: 10.1016/j.bjane.2022.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 04/18/2022] [Indexed: 11/22/2022]
Affiliation(s)
- André P Schmidt
- Hospital de Clínicas de Porto Alegre (HCPA), Serviço de Anestesia e Medicina Perioperatória, Porto Alegre, RS, Brazil; Universidade Federal do Rio Grande do Sul (UFRGS), Instituto de Ciências Básicas da Saúde (ICBS), Departamento de Bioquímica, Porto Alegre, RS, Brazil; Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Santa Casa de Porto Alegre, Serviço de Anestesia, Porto Alegre, RS, Brazil; Hospital Nossa Senhora da Conceição, Serviço de Anestesia, Porto Alegre, RS, Brazil; Universidade Federal do Rio Grande do Sul (UFRGS), Faculdade de Medicina, Programa de Pós-graduação em Ciências Pneumológicas, Porto Alegre, RS, Brazil; Faculdade de Medicina da Universidade de São Paulo (FMUSP), Programa de Pós-Graduação em Anestesiologia, Ciências Cirúrgicas e Medicina Perioperatória, São Paulo, SP, Brazil.
| | - Luciana C Stefani
- Universidade Federal do Rio Grande do Sul (UFRGS), Faculdade de Medicina, Departamento de Cirurgia, Porto Alegre, RS, Brazil
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Stahlschmidt A, Passos SC, Cardoso GR, Schuh GJ, Gutierrez CS, Castro SMJ, Caumo W, Pearse RM, Stefani LC. Enhanced peri-operative care to improve outcomes for high-risk surgical patients in Brazil: a single-centre before-and-after cohort study. Anaesthesia 2022; 77:416-427. [PMID: 35167136 DOI: 10.1111/anae.15671] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 01/02/2022] [Accepted: 01/05/2022] [Indexed: 01/02/2023]
Abstract
Mortality and morbidity for high-risk surgical patients are often high, especially in low-resource settings. Enhanced peri-operative care has the potential to reduce preventable deaths but must be designed to meet local needs. This before-and-after cohort study aimed to assess the effectiveness of a postoperative 48-hour enhanced care pathway for high-risk surgical patients ('high-risk surgical bundle') who did not meet the criteria for elective admission to intensive care. The pathway comprised of six elements: risk identification and communication; adoption of a high-risk post-anaesthesia care unit discharge checklist; prompt nursing admission to ward; intensification of vital signs monitoring; troponin measurement; and prompt access to medical support if required. The primary outcome was in-hospital mortality. Data describing 1189 patients from two groups, before and after implementation of the pathway, were compared. The usual care group comprised a retrospective cohort of high-risk surgical patients between September 2015 and December 2016. The intervention group prospectively included high-risk surgical patients from February 2019 to March 2020. Unadjusted mortality rate was 10.5% (78/746) for the usual care and 6.3% (28/443) for the intervention group. After adjustment, the intervention effect remained significant (RR 0.46 (95%CI 0.30-0.72). The high-risk surgical bundle group received more rapid response team calls (24% vs. 12.6%; RR 0.63 [95%CI 0.49-0.80]) and surgical re-interventions (18.9 vs. 7.5%; RR 0.41 [95%CI 0.30-0.59]). These data suggest that a clinical pathway based on enhanced surveillance for high-risk surgical patients in a resource-constrained setting could reduce in-hospital mortality.
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Affiliation(s)
- A Stahlschmidt
- Graduate Program in Medical Sciences, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - S C Passos
- Graduate Program in Medical Sciences, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - G R Cardoso
- School of Medicine, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil
| | - G J Schuh
- School of Medicine, Department of Surgery, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - C S Gutierrez
- Department of Surgery, Anaesthesia and Peri-operative Medicine Service, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - S M J Castro
- Department of Statistics, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - W Caumo
- Pain and Palliative Care Service, Laboratory of Pain and Neuromodulation, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - R M Pearse
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | | | - L C Stefani
- Department of Surgery, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
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de Souza Gutierrez C, Bottega K, de Jezus Castro SM, Gravina GL, Toralles EK, Silveira Martins OR, Caumo W, Stefani LC. The impact of the incorporation of a feasible postoperative mortality model at the Post-Anaesthestic Care Unit (PACU) on postoperative clinical deterioration: A pragmatic trial with 5,353 patients. PLoS One 2021; 16:e0257941. [PMID: 34780486 PMCID: PMC8592468 DOI: 10.1371/journal.pone.0257941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 09/14/2021] [Indexed: 11/19/2022] Open
Abstract
Background Practical use of risk predictive tools and the assessment of their impact on outcome reduction is still a challenge. This pragmatic study of quality improvement (QI) describes the preoperative adoption of a customised postoperative death probability model (SAMPE model) and the evaluation of the impact of a Postoperative Anaesthetic Care Unit (PACU) pathway on the clinical deterioration of high-risk surgical patients. Methods A prospective cohort of 2,533 surgical patients compared with 2,820 historical controls after the adoption of a quality improvement (QI) intervention. We carried out quick postoperative high-risk pathways at PACU when the probability of postoperative death exceeded 5%. As outcome measures, we used the number of rapid response team (RRT) calls within 7 and 30 postoperative days, in-hospital mortality, and non-planned Intensive Care Unit (ICU) admission. Results Not only did the QI succeed in the implementation of a customised risk stratification model, but it also diminished the postoperative deterioration evaluated by RRT calls on very high-risk patients within 30 postoperative days (from 23% before to 14% after the intervention, p = 0.05). We achieved no survival benefits or reduction of non-planned ICU. The small group of high-risk patients (13% of the total) accounted for the highest proportion of RRT calls and postoperative death. Conclusion Employing a risk predictive tool to guide immediate postoperative care may influence postoperative deterioration. It encouraged the design of pragmatic trials focused on feasible, low-technology, and long-term interventions that can be adapted to diverse health systems, especially those that demand more accurate decision making and ask for full engagement in the control of postoperative morbi-mortality.
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Affiliation(s)
- Claudia de Souza Gutierrez
- Postgraduate Program in Medical Sciences, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Anaesthesia and Perioperative Medicine Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Katia Bottega
- Anaesthesia and Perioperative Medicine Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | | | - Gabriela Leal Gravina
- Anaesthesia and Perioperative Medicine Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Eduardo Kohls Toralles
- Anaesthesia and Perioperative Medicine Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | | | - Wolnei Caumo
- Postgraduate Program in Medical Sciences, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Department of Surgery, School of Medicine, UFRGS, Porto Alegre, Brazil
- Laboratory of Pain & Neuromodulation, School of Medicine, UFRGS, Porto Alegre, Brazil
| | - Luciana Cadore Stefani
- Postgraduate Program in Medical Sciences, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Anaesthesia and Perioperative Medicine Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
- Department of Surgery, School of Medicine, UFRGS, Porto Alegre, Brazil
- * E-mail:
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12
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Passos SC, Stahlschmidt A, Blanco J, Spader ML, Brandão RB, Castro SMDJ, Gutierrez CDS, Silva Neto PCD, Stefani LPC. Derivation and validation of a national multicenter mortality risk stratification model - the ExCare model: a study protocol. Braz J Anesthesiol 2021; 72:316-321. [PMID: 34324938 PMCID: PMC9373516 DOI: 10.1016/j.bjane.2021.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 06/21/2021] [Accepted: 07/03/2021] [Indexed: 11/24/2022] Open
Abstract
Introduction Surgical care is essential for proper management of various diseases. However, it can result in unfavorable outcomes. In order to identify patients at higher risk of complications, several risk stratification models have been developed. Ideally, these tools should be simple, reproducible, accurate, and externally validated. Unfortunately, none of the best-known risk stratification instruments have been validated in Brazil. In this sense, the Ex-Care model was developed by retrospective data analysis of surgical patients in a major Brazilian university hospital. It consists of four independent predictors easily collected in the preoperative evaluation, showing high accuracy in predicting death within 30 days after surgery. Objectives To update and validate a Brazilian national-based model of postoperative death probability within 30 days based on the Ex-Care model. Also, to develop an application for smartphones that allows preoperative risk stratification by Ex-Care model. Methods Ten participating centers will collect retrospective data from digital databases. Variables age, American Society of Anesthesiologists (ASA) physical status, surgical severity (major or non-major) and nature (elective or urgent) will be evaluated as predictors for in-hospital mortality within 30 postoperative days, considered the primary outcome. Expected results We believe that the Ex-Care model will present discriminative capacity similar to other classically used scores validated for surgical mortality prediction. Furthermore, the mobile application to be developed will provide a practical and easy-to-use tool to the professionals enrolled in perioperative care.
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Affiliation(s)
- Sávio Cavalcante Passos
- Universidade Federal do Rio Grande do Sul (UFRGS), Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil.
| | - Adriene Stahlschmidt
- Universidade Federal do Rio Grande do Sul (UFRGS), Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - João Blanco
- Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
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