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OBJECTIVES: Despite an increasing proportion of patients undergoing lung resection being managed postoperatively in a ward-based environment, studies analyzing the impact of initial postoperative destination (IPD) on perioperative outcomes and unplanned critical care admission (UCCA) are lacking. DESIGN: A single-center retrospective review. SETTING: A cardiothoracic surgery center in the Northwest of England. PARTICIPANTS: A total of 3,841 patients between 2012 and 2018. INTERVENTIONS: All patients underwent lung resection. Patients were classified as either IPD ward or IPD critical care. MEASUREMENTS AND MAIN RESULTS: Outcomes assessed included in-hospital and 90-day mortality and UCCA. Differences in mortality rates between groups were assessed using the chi-square test. Multivariate logistic regression analyses were performed to identify variables independently associated with 90-day mortality and UCCA. In total, 23.8% (n = 913) of patients went to critical care as their IPD. Overall in-hospital mortality was 1.6% (n = 62), and 90-day mortality was 2.9% (n = 112). The rate of UCCA was 10.5% (n = 404) and was significantly higher for IPD ward patients compared to IPD critical care patients (11.9% v 6.2%, p < 0.001). The 90-day mortality rates after UCCA were 5.2% (IPD ward) and 19.3% (IPD critical care) (p < 0.001). Advanced age, worse pulmonary function, IPD ward, and timing of surgery were all independently associated with UCCA. CONCLUSIONS: Most patients undergoing lung resection can be managed safely postoperatively in a ward-based environment. Short-term mortality is higher after UCCA, with patients who experience readmission to critical care at the highest risk of death. Patients should receive additional monitoring immediately following discharge from critical care.
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Cuidados Críticos , Hospitalização , Mortalidade Hospitalar , Humanos , Pulmão , Readmissão do Paciente , Estudos Retrospectivos , Fatores de RiscoRESUMO
BACKGROUND: Accurately forecasting patients admitted to the intensive care units (ICUs) after surgery may improve clinical outcomes and guide the allocation of expensive and limited ICU resources. However, studies on predicting postoperative ICU admission in non-cardiac surgery have been limited. OBJECTIVE: To develop and validate a prediction model combining pre- and intraoperative variables to predict ICU admission after non-cardiac surgery. METHODS: This study is based on data from the Vital Signs DataBase (VitalDB) database. Predictors were selected using the least absolute shrinkage and selection operator regression method and logistic regression to develop a nomogram and an online web calculator. The model was internally verified by 1000-Bootstrap resampling. Performance of model was evaluated using area under the receiver operating characteristic curve (AUC), calibration curve and Brier score. The Youden's index was used to find the optimal nomogram's probability threshold. Clinical utility was assessed by decision curve analysis. RESULTS: This study included 5216 non-cardiac surgery patients; of these, 812 (15.6%) required postoperative ICU admission. Potential predictors included age, ASA classification, surgical department, emergency surgery, preoperative albumin level, preoperative urea nitrogen level, intraoperative crystalloid, intraoperative transfusion, intraoperative catheterization, and surgical time. A nomogram was constructed with an AUC of 0.917 (95% CI: 0.907-0.926) and a Brier score of 0.077. The Bootstrap-adjusted AUC was 0.914; the adjusted Brier score was 0.078. The calibration curve showed good agreement between predicted and actual probabilities; and the decision curve indicated clinical usefulness. Finally, we established an online web calculator for clinical application (https://xuzhikun.shinyapps.io/postopICUadmission1/). CONCLUSION: We developed and internally validated an easy-to-use nomogram for predicting ICU admission after non-cardiac surgery.
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Hospitalização , Unidades de Terapia Intensiva , Humanos , Bases de Dados Factuais , Período Pós-Operatório , Curva ROC , Estudos RetrospectivosRESUMO
This study aimed to characterize patients admitted to critical care following Emergency Department (ED) presentation with acute recreational drug toxicity and to identify determinants of admission to critical care. A retrospective multicenter matched case-control study was conducted by the European Drug Emergency Network Plus (Euro-DEN Plus) over the period 2014-2021. The cases were ED presentations with acute recreational drug toxicity admitted to critical care, the controls consisted of ED presentations with acute recreational drug toxicity medically discharged directly from the ED. The potential determinants of admission to critical care were assessed through multivariable conditional stepwise logistic regression analysis and multiple imputation was used to account for the missing data. From 2014 to 2021, 3448 Euro-DEN Plus presentations involved patients admitted to critical care (76.9% males; mean age 33.2 years; SD 10.9 years). Patient age ≥35 years (as compared to ≤18 years) was a determinant of admission to critical care following acute recreational drug toxicity (adjusted odds ratio, aOR, 1.51, 95% confidence interval, CI, 1.15-1.99), along with polydrug use (aOR 1.39, 95% CI 1.22-1.59), ethanol co-ingestion (aOR 1.44, 95% CI 1.26-1.64), and the use of gamma-hydroxybutyrate/gamma-butyrolactone (GHB/GBL, aOR 3.08, 95% CI 2.66-3.57). Conversely, lower odds of admission to critical care were associated with the use of cocaine (aOR 0.85, 95% CI 0.74-0.99), cannabis (aOR 0.44, 95% CI 0.37-0.52), heroin (aOR 0.80, 95% CI 0.69-0.93), and amphetamine (aOR 0.65, 95% CI 0.54-0.78), as was the arrival to the ED during the night (8 p.m.-8 a.m., aOR 0.88, 95% CI 0.79-0.98). These findings, which deserve confirmation and further investigation, could contribute to a more complete understanding of the decision-making process underlying the admission to critical care of patients with acute recreational drug toxicity.
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IMPORTANCE: Hospitalizations are associated with cognitive decline in older adults. OBJECTIVE: To determine the association between hospitalization characteristics and the trajectory of cognitive function in older adults. DESIGN: Population-based longitudinal study of cognitive aging. SETTING: Olmsted Medical Center and Mayo Clinic, the only centers in Olmsted County, Minnesota, with hospitalization capacity. PARTICIPANTS: Individuals without dementia at baseline, with consecutive cognitive assessments from 2004 through 2017, and at least one visit after the age of 60. MEASUREMENTS: The primary outcome was longitudinal changes in global cognitive z-score. Secondary outcomes were changes in four cognitive domains: memory, attention/executive function, language, and visuospatial skills. Hospitalization characteristics analyzed included elective versus nonelective, medical versus surgical, critical care versus no critical care admission, and long versus short duration admissions. RESULTS: Of 4,587 participants, 1,622 had 1 and more hospital admission. Before hospitalization, the average slope of the global z-score was -0.031 units/year. After hospitalization, the rate of annual global z-score accelerated by -0.051 (95% CI = -0.057, -0.045) units, P < .001, resulting in an estimated annual slope after the first hospitalization of -0.082. The accelerated decline was found in all four cognitive domains (memory, visuospatial, language, and executive, all P < .001). The acceleration of the decline in global z-score following hospitalization was greater for medical compared to surgical hospitalizations (slope change following hospitalization = -0.064 vs -0.034 for medical vs surgical, P < .001), and nonelective compared to elective admissions (slope change following hospitalization = -0.075 vs -0.037 for nonelective vs elective, P < .001). The acceleration of cognitive decline was not different for hospitalization with intensive care unit admission versus not. CONCLUSIONS: Hospitalization of older adults is associated with accelerated decline of global and domain-specific cognitive domains, with the rate of decline dependent upon type of admission. The clinical impact of this accelerated decline will depend on the individual's baseline cognitive reserve and expected longevity.