RESUMO
OBJECTIVE: We sought to evaluate how implementing a thoracic enhanced recovery after surgery (ERAS) protocol impacted surgical outcomes after elective anatomic lung resection. BACKGROUND: The effect of implementing the ERAS Society/European Society of Thoracic Surgery thoracic ERAS protocol on postoperative outcomes throughout an entire health care system has not yet been reported. METHODS: This was a prospective cohort study within one health care system (January 2019-March, 2023). A thoracic ERAS protocol was implemented on May 1, 2021 for elective anatomic lung resections, and postoperative outcomes were tracked using the electronic health record and Vizient data. The primary outcome was overall morbidity; secondary outcomes included individual complications, length of stay, opioid use, chest tube duration, and total cost. Patients were grouped into pre-ERAS and post-ERAS cohorts. Bivariable comparisons were performed using independent t -test, χ 2 , or Fisher exact tests, and multivariable logistic regression was performed to control for confounders. RESULTS: There were 1007 patients in the cohort; 450 (44.7%) were in the post-ERAS group. Mean age was 66.2 years; most patients were female (65.1%), white (83.8%), had a body mass index between 18.5 and 29.9 (69.7%), and were ASA class 3 (80.6%). Patients in the postimplementation group had lower risk-adjusted rates of any morbidity, respiratory complication, pneumonia, surgical site infection, arrhythmias, infections, opioid usage, ICU use, and shorter postoperative length of stay (all P <0.05). CONCLUSIONS: Postoperative outcomes were improved after the implementation of an evidence-based thoracic ERAS protocol throughout the health care system. This study validates the ERAS Society/European Society of Thoracic Surgery guidelines and demonstrates that simultaneous multihospital implementation can be feasible and effective.
Assuntos
Recuperação Pós-Cirúrgica Melhorada , Pneumonectomia , Complicações Pós-Operatórias , Humanos , Feminino , Masculino , Idoso , Estudos Prospectivos , Pneumonectomia/métodos , Complicações Pós-Operatórias/epidemiologia , Complicações Pós-Operatórias/prevenção & controle , Pessoa de Meia-Idade , Protocolos Clínicos , Tempo de Internação/estatística & dados numéricosRESUMO
BACKGROUND: Thirty-day mortality after outpatient surgery is unexpected and undesired. We investigated preoperative risk factors, operative variables, and postoperative complications associated with 30-day death after outpatient surgery. METHODS: Using the 2005 to 2018 American College of Surgeons National Surgical Quality Improvement Program database, we evaluated 30-day mortality rate trends over time after outpatient operations. We analyzed associations between 37 preoperative variables, operation time, hospital length of stay, and 9 postoperative complications with mortality rate using χ2 analyses for categorical data and tests for continuous data. We used forward selection logistic regression models to determine the best predictors of mortality preoperatively and postoperatively. We also separately analyzed mortality by age group. RESULTS: A total of 2,822,789 patients were included. The 30-day mortality rate did not change significantly over time (P = .34, Cochran-Armitage trend test), remaining steady at around 0.06%. The most significant preoperative predictors of mortality included the patient having disseminated cancer, decreased functional health status, increased American Society of Anesthesiology Physical Status classification, increased age, and ascites, accounting for 95.8% (0.837/0.874) of the full model c-index. The most significant postoperative complications associated with increased risk of mortality included having cardiac (26.95% yes vs 0.04% no), pulmonary (10.25% vs 0.04%), stroke (9.22% vs 0.06%), and renal (9.33% vs 0.06%) complications. Postoperative complications conferred a greater risk for mortality than preoperative variables. Mortality risk increased incrementally with age, particularly past age 80. CONCLUSION: The operative mortality rate after outpatient surgery has not changed over time. Patients over 80 years with disseminated cancer, decreased functional health status, or increased ASA class should generally be considered for inpatient surgery. However, there might be some circumstances where outpatient surgery could be considered.
Assuntos
Procedimentos Cirúrgicos Ambulatórios , Pacientes Internados , Humanos , Estados Unidos/epidemiologia , Idoso de 80 Anos ou mais , Procedimentos Cirúrgicos Ambulatórios/efeitos adversos , Fatores de Risco , Modelos Logísticos , Complicações Pós-Operatórias/epidemiologia , Complicações Pós-Operatórias/etiologia , Melhoria de Qualidade , Estudos RetrospectivosRESUMO
BACKGROUND: Present at the time of surgery (PATOS) is an important measure to collect in postoperative complication surveillance systems because it may affect a patient's risk of a subsequent complication and the estimation of postoperative complication rates attributed to the healthcare system. The American College of Surgeons (ACS) NSQIP started collecting PATOS data for 8 postoperative complications in 2011, but no one has used these data to quantify how this may affect unadjusted and risk-adjusted postoperative complication rates. STUDY DESIGN: This study was a retrospective observational study of the ACS NSQIP database from 2012 to 2018. PATOS data were analyzed for the 8 postoperative complications of superficial, deep, and organ space surgical site infection; pneumonia; urinary tract infection; ventilator dependence; sepsis; and septic shock. Unadjusted postoperative complication rates were compared ignoring PATOS vs taking PATOS into account. Observed to expected ratios over time were also compared by calculating expected values using multiple logistic regression analyses with complication as the dependent variable and the 28 nonlaboratory preoperative variables in the ACS NSQIP database as the independent variables. RESULTS: In 5,777,108 patients, observed event rates for each outcome were reduced by between 6.1% (superficial surgical site infection) and 52.5% (sepsis) when PATOS was taken into account. The observed to expected ratios were similar each year for all outcomes, except for sepsis and septic shock in the early years. CONCLUSIONS: Taking PATOS into account is important for reporting unadjusted event rates. The effect varied by type of complication-lowest for superficial surgical site infection and highest for sepsis and septic shock. Taking PATOS into account was less important for risk-adjusted outcomes (observed to expected ratios), except for sepsis and septic shock.
Assuntos
Sepse , Choque Séptico , Humanos , Infecção da Ferida Cirúrgica/epidemiologia , Infecção da Ferida Cirúrgica/etiologia , Choque Séptico/epidemiologia , Choque Séptico/complicações , Estudos Retrospectivos , Bases de Dados Factuais , Sepse/epidemiologia , Complicações Pós-Operatórias/epidemiologia , Complicações Pós-Operatórias/etiologia , Fatores de RiscoRESUMO
BACKGROUND: Emerging literature suggests that measures of social vulnerability should be incorporated into surgical risk calculators. The Social Vulnerability Index (SVI) is a measure designed by the CDC that encompasses 15 socioeconomic and demographic variables at the census tract level. We examined whether adding the SVI into a parsimonious surgical risk calculator would improve model performance. STUDY DESIGN: The eight-variable Surgical Risk Preoperative Assessment System (SURPAS), developed using the entire American College of Surgeons (ACS) NSQIP database, was applied to local ACS-NSQIP data from 2012 to 2018 to predict 12 postoperative outcomes. Patient addresses were geocoded and used to estimate the SVI, which was then added to the model as a ninth predictor variable. Brier scores and c-indices were compared for the models with and without the SVI. RESULTS: The analysis included 31,222 patients from five hospitals. Brier scores were identical for eight outcomes and improved by only one to two points in the fourth decimal place for four outcomes with addition of the SVI. Similarly, c-indices were not significantly different (p values ranged from 0.15 to 0.96). Of note, the SVI was associated with most of the eight SURPAS predictor variables, suggesting that SURPAS may already indirectly capture this important risk factor. CONCLUSION: The eight-variable SURPAS prediction model was not significantly improved by adding the SVI, showing that this parsimonious tool functions well without including a measure of social vulnerability.
Assuntos
Complicações Pós-Operatórias , Vulnerabilidade Social , Bases de Dados Factuais , Humanos , Estudos Retrospectivos , Medição de Risco , Fatores de RiscoRESUMO
BACKGROUND: Postoperative complications, length of index hospital stay, and unplanned hospital readmissions are important metrics reflecting surgical care quality. Postoperative infections represent a substantial proportion of all postoperative complications. We examined the relationships between identification of postoperative infection prehospital and posthospital discharge, length of stay, and unplanned readmissions in the American College of Surgeons National Surgical Quality Improvement Program database across nine surgical specialties. METHODS: The 30-day postoperative infectious complications including sepsis, surgical site infections, pneumonia, and urinary tract infection were analyzed in the American College of Surgeons National Surgical Quality Improvement Program inpatient data during the period from 2012 to 2017. General, gynecologic, vascular, orthopedic, otolaryngology, plastic, thoracic, urologic, and neurosurgical inpatient operations were selected. RESULTS: Postoperative infectious complications were identified in 5.2% (137,014/2,620,450) of cases; 81,929 (59.8%) were postdischarge. The percentage of specific complications identified postdischarge were 73.4% of surgical site infections (range across specialties 63.7-93.1%); 34.9% of sepsis cases (27.4-58.1%); 26.5% of pneumonia cases (18.9%-36.3%); and 53.2% of urinary tract infections (48.3%-88.0%). The relative risk of readmission among patients with postdischarge versus predischarge surgical site infection, sepsis, pneumonia, or urinary tract infection was 5.13 (95% confidence interval: 4.90-5.37), 9.63 (8.93-10.40), 10.79 (10.15-11.45), and 3.32 (3.07-3.60), respectively. Over time, mean length of stay decreased but postdischarge infections and readmission rates significantly increased. CONCLUSION: Most postoperative infectious complications were diagnosed postdischarge. These were associated with an increased risk of readmission. The trend toward shorter length of stay over time was observed along with an increase both in the percentage of infections detected after discharge and the rate of unplanned related postoperative readmissions over time. Postoperative surveillance of infections should extend beyond hospital discharge of surgical patients.
Assuntos
Assistência ao Convalescente/organização & administração , Complicações Pós-Operatórias/epidemiologia , Melhoria de Qualidade/estatística & dados numéricos , Centro Cirúrgico Hospitalar/organização & administração , Procedimentos Cirúrgicos Operatórios/efeitos adversos , Adulto , Assistência ao Convalescente/estatística & dados numéricos , Idoso , Bases de Dados Factuais/estatística & dados numéricos , Feminino , Humanos , Tempo de Internação/estatística & dados numéricos , Masculino , Pessoa de Meia-Idade , Alta do Paciente/estatística & dados numéricos , Readmissão do Paciente/estatística & dados numéricos , Pneumonia/epidemiologia , Pneumonia/etiologia , Complicações Pós-Operatórias/etiologia , Fatores de Risco , Sepse/epidemiologia , Sepse/etiologia , Centro Cirúrgico Hospitalar/estatística & dados numéricos , Infecção da Ferida Cirúrgica/epidemiologia , Infecção da Ferida Cirúrgica/etiologia , Estados Unidos/epidemiologia , Infecções Urinárias/epidemiologia , Infecções Urinárias/etiologiaRESUMO
BACKGROUND: The objective of this study was to determine the effects of using the Surgical Risk Preoperative Assessment System (SURPAS) on patient satisfaction and surgeon efficiency in the surgical informed consent process, as compared to surgeons' "usual" consent process. STUDY DESIGN: Patient perception of the consent process was assessed via survey in 2 cohorts: 10 surgeons in different specialties used their "usual" consent process for 10 patients; these surgeons were then taught to use SURPAS, and they used it during the informed consent process of 10 additional patients. The data were compared using Fisher's exact test and the Cochran-Mantel-Haenszel test. RESULTS: One hundred patients underwent the "usual" consent process (USUAL), and 93 underwent SURPAS-guided consent (SURPAS). Eighty-two percent of SURPAS were "very satisfied" and 18% were "satisfied" with risk discussion vs 16% and 72% of USUAL, respectively. Of those who used SURPAS, 75.3% reported the risk discussion made them "more comfortable" with surgery vs 19% of USUAL, and 90.3% of SURPAS users reported "somewhat" or "greatly decreased" anxiety vs 20% of USUAL. All p values were <0.0001. Among SURPAS patients, 97.9% reported "enough time spent discussing risks" vs 72.0% of USUAL patients. CONCLUSIONS: The SURPAS tool improved the informed consent process for patients compared with the "usual" consent process, in terms of patient satisfaction, ie making patients feel more comfortable and less anxious about their impending operations. Providers should consider integrating the SURPAS tool into their preoperative consent process.
Assuntos
Consentimento Livre e Esclarecido , Satisfação do Paciente , Complicações Pós-Operatórias/epidemiologia , Cuidados Pré-Operatórios , Adulto , Idoso , Estudos de Coortes , Tomada de Decisões , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Medição de Risco , Inquéritos e QuestionáriosRESUMO
BACKGROUND: With inpatient length of stay decreasing, discharge destination after surgery can serve as an important metric for quality of care. Additionally, patients desire information on possible discharge destination. Adequate planning requires a multidisciplinary approach, can reduce healthcare costs and ensure patient needs are met. The Surgical Risk Preoperative Assessment System (SURPAS) is a parsimonious risk assessment tool using 8 predictor variables developed from the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) dataset. SURPAS is applicable to more than 3,000 operations in adults in 9 surgical specialties, predicts important adverse outcomes, and is incorporated into our electronic health record. We sought to determine whether SURPAS can accurately predict discharge destination. STUDY DESIGN: A "full model" for risk of postoperative "discharge not to home" was developed from 28 nonlaboratory preoperative variables from ACS NSQIP 2012-2017 dataset using logistic regression. This was compared with the 8-variable SURPAS model using the C index as a measure of discrimination, the Hosmer-Lemeshow observed-to-expected plots testing calibration, and the Brier score, a combined metric of discrimination and calibration. RESULTS: Of 5,303,519 patients, 447,153 (8.67%) experienced a discharge not to home. The SURPAS model's C index, 0.914, was 99.24% of that of the full model's (0.921); the Hosmer-Lemeshow plots indicated good calibration and the Brier score was 0.0537 and 0.0514 for the SUPAS and full model, respectively. CONCLUSIONS: The 8-variable SURPAS model preoperatively predicts risk of postoperative discharge to a destination other than home as accurately as the 28 nonlaboratory variable ACS NSQIP full model. Therefore, discharge destination can be integrated into the existing SURPAS tool, providing accurate outcomes to guide decision-making and help prepare patients for their postoperative recovery.
Assuntos
Modelos Estatísticos , Alta do Paciente , Transferência de Pacientes/estatística & dados numéricos , Procedimentos Cirúrgicos Operatórios , Adulto , Idoso , Idoso de 80 Anos ou mais , Feminino , Previsões , Humanos , Masculino , Pessoa de Meia-Idade , Período Pré-Operatório , Melhoria de Qualidade , Reprodutibilidade dos Testes , Medição de RiscoRESUMO
OBJECTIVE: To develop parsimonious prediction models for postoperative mortality, overall morbidity, and 6 complication clusters applicable to a broad range of surgical operations in adult patients. SUMMARY BACKGROUND DATA: Quantitative risk assessment tools are not routinely used for preoperative patient assessment, shared decision making, informed consent, and preoperative patient optimization, likely due in part to the burden of data collection and the complexity of incorporation into routine surgical practice. METHODS: Multivariable forward selection stepwise logistic regression analyses were used to develop predictive models for 30-day mortality, overall morbidity, and 6 postoperative complication clusters, using 40 preoperative variables from 2,275,240 surgical cases in the American College of Surgeons National Surgical Quality Improvement Program data set, 2005 to 2012. For the mortality and overall morbidity outcomes, prediction models were compared with and without preoperative laboratory variables, and generic models (based on all of the data from 9 surgical specialties) were compared with specialty-specific models. In each model, the cumulative c-index was used to examine the contribution of each added predictor variable. C-indexes, Hosmer-Lemeshow analyses, and Brier scores were used to compare discrimination and calibration between models. RESULTS: For the mortality and overall morbidity outcomes, the prediction models without the preoperative laboratory variables performed as well as the models with the laboratory variables, and the generic models performed as well as the specialty-specific models. The c-indexes were 0.938 for mortality, 0.810 for overall morbidity, and for the 6 complication clusters ranged from 0.757 for infectious to 0.897 for pulmonary complications. Across the 8 prediction models, the first 7 to 11 variables entered accounted for at least 99% of the c-index of the full model (using up to 28 nonlaboratory predictor variables). CONCLUSIONS: Our results suggest that it will be possible to develop parsimonious models to predict 8 important postoperative outcomes for a broad surgical population, without the need for surgeon specialty-specific models or inclusion of laboratory variables.
Assuntos
Mortalidade Hospitalar , Cuidados Pós-Operatórios , Cuidados Pré-Operatórios , Adulto , Humanos , Modelos Logísticos , Medição de Risco/métodos , Fatores de Risco , CirurgiõesRESUMO
OBJECTIVE: To develop accurate preoperative risk prediction models for multiple adverse postoperative outcomes applicable to a broad surgical population using a parsimonious common set of risk variables and outcomes. SUMMARY BACKGROUND DATA: Currently, preoperative assessment of surgical risk is largely based on subjective clinician experience. We propose a paradigm shift from the current postoperative risk adjustment for cross-hospital comparison to patient-centered quantitative risk assessment during the preoperative evaluation. METHODS: We identify the most common and important predictor variables of postoperative mortality, overall morbidity, and 6 complication clusters from previously published prediction analyses that used forward selection stepwise logistic regression. We then refit the prediction models using only the 8 most common and important predictor variables, and compare the discrimination and calibration of these models to the original full-variable models using the c-index, Hosmer-Lemeshow analysis, and Brier scores. RESULTS: Accurate risk models for 30-day outcomes of mortality, overall morbidity, and 6 clusters of complications were developed using a set of 8 preoperative risk variables. C-indexes of the 8 variable models are between 97.9% and 99.2% of those of the full models containing up to 28 variables, indicating excellent discrimination using fewer predictor variables. Hosmer-Lemeshow analyses showed observed to expected event rates to be nearly identical between parsimonious models and full models, both showing good calibration. CONCLUSIONS: Accurate preoperative risk assessment of postoperative mortality, overall morbidity, and 6 complication clusters in a broad surgical population can be achieved with as few as 8 preoperative predictor variables, improving feasibility of routine preoperative risk assessment for surgical patients.