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Preoperative depression is an underappreciated comorbidity that has important implications for postoperative outcomes. Screening for symptoms of depression before surgery can identify patients with or without a previous diagnosis of depression who could benefit from perioperative interventions to improve mood. Preoperative screening programmes are feasible to implement, although care must be taken to ensure that patients who are most likely to benefit are included.
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Depressão , Cuidados Pré-Operatórios , Humanos , Cuidados Pré-Operatórios/métodos , Depressão/diagnóstico , Programas de Rastreamento/métodos , Complicações Pós-Operatórias/diagnóstico , Transtorno Depressivo/diagnósticoRESUMO
Importance: Intraoperative electroencephalogram (EEG) waveform suppression, suggesting excessive general anesthesia, has been associated with postoperative delirium. Objective: To assess whether EEG-guided anesthesia decreases the incidence of delirium after cardiac surgery. Design, Setting, and Participants: Randomized, parallel-group clinical trial of 1140 adults 60 years or older undergoing cardiac surgery at 4 Canadian hospitals. Recruitment was from December 2016 to February 2022, with follow-up until February 2023. Interventions: Patients were randomized in a 1:1 ratio (stratified by hospital) to receive EEG-guided anesthesia (n = 567) or usual care (n = 573). Patients and those assessing outcomes were blinded to group assignment. Main Outcomes and Measures: The primary outcome was delirium during postoperative days 1 through 5. Intraoperative measures included anesthetic concentration and EEG suppression time. Secondary outcomes included intensive care and hospital length of stay. Serious adverse events included intraoperative awareness, medical complications, and 30-day mortality. Results: Of 1140 randomized patients (median [IQR] age, 70 [65-75] years; 282 [24.7%] women), 1131 (99.2%) were assessed for the primary outcome. Delirium during postoperative days 1 to 5 occurred in 102 of 562 patients (18.15%) in the EEG-guided group and 103 of 569 patients (18.10%) in the usual care group (difference, 0.05% [95% CI, -4.57% to 4.67%]). In the EEG-guided group compared with the usual care group, the median volatile anesthetic minimum alveolar concentration was 0.14 (95% CI, 0.15 to 0.13) lower (0.66 vs 0.80) and there was a 7.7-minute (95% CI, 10.6 to 4.7) decrease in the median total time spent with EEG suppression (4.0 vs 11.7 min). There were no significant differences between groups in median length of intensive care unit (difference, 0 days [95% CI, -0.31 to 0.31]) or hospital stay (difference, 0 days [95% CI, -0.94 to 0.94]). No patients reported intraoperative awareness. Medical complications occurred in 64 of 567 patients (11.3%) in the EEG-guided group and 73 of 573 (12.7%) in the usual care group. Thirty-day mortality occurred in 8 of 567 patients (1.4%) in the EEG-guided group and 13 of 573 (2.3%) in the usual care group. Conclusions and Relevance: Among older adults undergoing cardiac surgery, EEG-guided anesthetic administration to minimize EEG suppression, compared with usual care, did not decrease the incidence of postoperative delirium. This finding does not support EEG-guided anesthesia for this indication. Trial Registration: ClinicalTrials.gov Identifier: NCT02692300.
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Anestesia Geral , Procedimentos Cirúrgicos Cardíacos , Eletroencefalografia , Humanos , Feminino , Idoso , Masculino , Procedimentos Cirúrgicos Cardíacos/efeitos adversos , Canadá , Anestesia Geral/efeitos adversos , Complicações Pós-Operatórias/prevenção & controle , Complicações Pós-Operatórias/epidemiologia , Pessoa de Meia-Idade , Tempo de Internação , Delírio do Despertar/prevenção & controle , Delírio do Despertar/epidemiologia , Delírio/prevenção & controle , Delírio/epidemiologia , Delírio/etiologia , IncidênciaRESUMO
Background: Anaesthesiology clinicians can implement risk mitigation strategies if they know which patients are at greatest risk for postoperative complications. Although machine learning models predicting complications exist, their impact on clinician risk assessment is unknown. Methods: This single-centre randomised clinical trial enrolled patients age ≥18 undergoing surgery with anaesthesiology services. Anaesthesiology clinicians providing remote intraoperative telemedicine support reviewed electronic health records with (assisted group) or without (unassisted group) also reviewing machine learning predictions. Clinicians predicted the likelihood of postoperative 30-day all-cause mortality and postoperative acute kidney injury within 7 days. Area under the receiver operating characteristic curve (AUROC) for the clinician predictions was determined. Results: Among 5,071 patient cases reviewed by 89 clinicians, the observed incidence was 2% for postoperative death and 11% for acute kidney injury. Clinician predictions agreed with the models more strongly in the assisted versus unassisted group (weighted kappa 0.75 versus 0.62 for death [difference 0.13, 95%CI 0.10-0.17] and 0.79 versus 0.54 for kidney injury [difference 0.25, 95%CI 0.21-0.29]). Clinicians predicted death with AUROC of 0.793 in the assisted group and 0.780 in the unassisted group (difference 0.013, 95%CI -0.070 to 0.097). Clinicians predicted kidney injury with AUROC of 0.734 in the assisted group and 0.688 in the unassisted group (difference 0.046, 95%CI -0.003 to 0.091). Conclusions: Although there was evidence that the models influenced clinician predictions, clinician performance was not statistically significantly different with and without machine learning assistance. Further work is needed to clarify the role of machine learning in real-time perioperative risk stratification. Trial Registration: ClinicalTrials.gov NCT05042804.
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Background: Novel applications of telemedicine can improve care quality and patient outcomes. Telemedicine for intraoperative decision support has not been rigorously studied. Methods: This single centre randomised clinical trial ( clinicaltrials.gov NCT03923699 ) of unselected adult surgical patients was conducted between July 1, 2019 and January 31, 2023. Patients received usual care or decision support from a telemedicine service, the Anesthesiology Control Tower (ACT). The ACT provided real-time recommendations to intraoperative anaesthesia clinicians based on case reviews, machine-learning forecasting, and physiologic alerts. ORs were randomised 1:1. Co-primary outcomes of 30-day all-cause mortality, respiratory failure, acute kidney injury (AKI), and delirium were analysed as intention-to-treat. Results: The trial completed planned enrolment with 71927 surgeries (35956 ACT; 35971 usual care). After multiple testing correction, there was no significant effect of the ACT vs. usual care on 30-day mortality [641/35956 (1.8%) vs 638/35971 (1.8%), risk difference 0.0% (95% CI -0.2% to 0.3%), p=0.96], respiratory failure [1089/34613 (3.1%) vs 1112/34619 (3.2%), risk difference -0.1% (95% CI -0.4% to 0.3%), p=0.96], AKI [2357/33897 (7%) vs 2391/33795 (7.1%), risk difference -0.1% (-0.6% to 0.4%), p=0.96], or delirium [1283/3928 (32.7%) vs 1279/3989 (32.1%), risk difference 0.6% (-2.0% to 3.2%), p=0.96]. There were no significant differences in secondary outcomes or in sensitivity analyses. Conclusions: In this large RCT of a novel application of telemedicine-based remote monitoring and decision support using real-time alerts and case reviews, we found no significant differences in postoperative outcomes. Large-scale intraoperative telemedicine is feasible, and we suggest future avenues where it may be impactful.
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BACKGROUND: Older surgical patients with depression often experience poor postoperative outcomes. Poor outcomes may stem from brain-hazardous medications and subadequate antidepressant dosing. METHODS: This was a retrospective, observational cohort study covering the period between January 1, 2021 and December 31, 2021. Patients ≥60 years of age who underwent inpatient surgery and had an overnight stay at an integrated academic health care system comprising 14 hospitals were eligible. We analyzed the prevalence of home central nervous system (CNS)-active potentially inappropriate medication (PIM) and potential subadequate antidepressant dosing in older surgical patients receiving home antidepressants. Univariable and multivariable regression models were used to identify factors associated with home CNS-active PIM prescribing and potential subadequate antidepressant dosing. Additionally, outcomes were compared among patients receiving and not receiving CNS-active PIMs and patients receiving and not receiving subadequate antidepressant dosing. RESULTS: A total of 8031 patients were included in this study (47% female, mean age = 70 years) of whom 2087 (26%) were prescribed antidepressants. Roughly one-half (49%, 95% confidence interval [CI], 46.5-50.1) of patients receiving home antidepressants were also receiving ≥1 CNS-active PIM and 29% (95% CI, 27.0-29.3) were receiving a potential subadequate dose. Factors associated with an increased likelihood of receiving a home CNS-active PIM included female sex (adjusted odds ratio [aOR], 1.46), anxiety (aOR, 2.43), asthma or chronic obstructive pulmonary disease (aOR, 1.39), and serotonin-norepinephrine reuptake inhibitor use (aOR, 1.54). Patients aged ≥75 years (aOR, 1.57), black race (aOR, 1.48) and those with congestive heart failure (aOR, 1.33) were more likely to be prescribed a potential subadequate antidepressant dose. Patients receiving potential subadequate antidepressant doses were discharged home less often (64% vs 73%), had a longer hospital length of stay (9 days vs 7 days), and a higher mortality rate (18% vs 10%) compared to patients receiving adequate home antidepressant doses (P-value for all <0.01). No differences in these outcomes were found among patients receiving home antidepressants with or without CNS-active PIMs. CONCLUSIONS: Older surgical patients receiving antidepressants are frequently prescribed brain-hazardous medications and potentially subadequate antidepressant doses. Those receiving subadequate antidepressant doses may be at risk for worse postoperative outcomes compared to patients receiving adequate doses. The role of preoperative medication optimization to improve outcomes for older surgical patients should be evaluated.
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Antidepressivos , Humanos , Feminino , Masculino , Idoso , Antidepressivos/administração & dosagem , Antidepressivos/uso terapêutico , Estudos Retrospectivos , Pessoa de Meia-Idade , Idoso de 80 Anos ou mais , Estados Unidos/epidemiologia , Prescrição Inadequada , Depressão/tratamento farmacológico , Depressão/diagnóstico , Depressão/psicologia , Lista de Medicamentos Potencialmente Inapropriados , Fatores de Risco , Procedimentos Cirúrgicos Operatórios/efeitos adversos , Fatores EtáriosRESUMO
OBJECTIVE: An applied problem facing all areas of data science is harmonizing data sources. Joining data from multiple origins with unmapped and only partially overlapping features is a prerequisite to developing and testing robust, generalizable algorithms, especially in healthcare. This integrating is usually resolved using meta-data such as feature names, which may be unavailable or ambiguous. Our goal is to design methods that create a mapping between structured tabular datasets derived from electronic health records independent of meta-data. METHODS: We evaluate methods in the challenging case of numeric features without reliable and distinctive univariate summaries, such as nearly Gaussian and binary features. We assume that a small set of features are a priori mapped between two datasets, which share unknown identical features and possibly many unrelated features. Inter-feature relationships are the main source of identification which we expect. We compare the performance of contrastive learning methods for feature representations, novel partial auto-encoders, mutual-information graph optimizers, and simple statistical baselines on simulated data, public datasets, the MIMIC-III medical-record changeover, and perioperative records from before and after a medical-record system change. Performance was evaluated using both mapping of identical features and reconstruction accuracy of examples in the format of the other dataset. RESULTS: Contrastive learning-based methods overall performed the best, often substantially beating the literature baseline in matching and reconstruction, especially in the more challenging real data experiments. Partial auto-encoder methods showed on-par matching with contrastive methods in all synthetic and some real datasets, along with good reconstruction. However, the statistical method we created performed reasonably well in many cases, with much less dependence on hyperparameter tuning. When validating feature match output in the EHR dataset we found that some mistakes were actually a surrogate or related feature as reviewed by two subject matter experts. CONCLUSION: In simulation studies and real-world examples, we find that inter-feature relationships are effective at identifying matching or closely related features across tabular datasets when meta-data is not available. Decoder architectures are also reasonably effective at imputing features without an exact match.
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Algoritmos , Registros Eletrônicos de Saúde , Simulação por Computador , Ciência de Dados , MotivaçãoRESUMO
Background: Surgical patients with previous depression frequently experience postoperative depressive symptoms. This study's objective was to determine the feasibility of a placebo-controlled trial testing the impact of a sustained ketamine infusion on postoperative depressive symptoms. Methods: This single-centre, triple-blind, placebo-controlled randomised clinical trial included adult patients with depression scheduled for inpatient surgery. After surgery, patients were randomly allocated to receive ketamine (0.5 mg kg-1 over 10 min followed by 0.3 mg kg-1 h-1 for 3 h) or an equal volume of normal saline. Depressive symptoms were measured using the Montgomery-Asberg Depression Rating Scale. On post-infusion day 1, participants guessed which intervention they received. Feasibility endpoints included the fraction of patients approached who were randomised, the fraction of randomised patients who completed the study infusion, and the fraction of scheduled depression assessments that were completed. Results: In total, 32 patients were allocated a treatment, including 31/101 patients approached after a protocol change (31%, 1.5 patients per week). The study infusion was completed without interruption in 30/32 patients (94%). In each group, 7/16 participants correctly guessed which intervention they received. Depression assessments were completed at 170/192 scheduled time points (89%). Between baseline and post-infusion day 4 (pre-specified time point of interest), median depressive symptoms decreased in both groups, with difference-in-differences of -1.00 point (95% confidence interval -3.23 to 1.73) with ketamine compared with placebo. However, the between-group difference did not persist at other time points. Conclusions: Patient recruitment, medication administration, and clinical outcome measurement appear to be highly feasible, with blinding maintained. A fully powered trial may be warranted. Clinical trial registration: NCT05233566.
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BACKGROUND: Machine learning models can help anesthesiology clinicians assess patients and make clinical and operational decisions, but well-designed human-computer interfaces are necessary for machine learning model predictions to result in clinician actions that help patients. Therefore, the goal of this study was to apply a user-centered design framework to create a user interface for displaying machine learning model predictions of postoperative complications to anesthesiology clinicians. METHODS: Twenty-five anesthesiology clinicians (attending anesthesiologists, resident physicians, and certified registered nurse anesthetists) participated in a 3-phase study that included (phase 1) semistructured focus group interviews and a card sorting activity to characterize user workflows and needs; (phase 2) simulated patient evaluation incorporating a low-fidelity static prototype display interface followed by a semistructured interview; and (phase 3) simulated patient evaluation with concurrent think-aloud incorporating a high-fidelity prototype display interface in the electronic health record. In each phase, data analysis included open coding of session transcripts and thematic analysis. RESULTS: During the needs assessment phase (phase 1), participants voiced that (a) identifying preventable risk related to modifiable risk factors is more important than nonpreventable risk, (b) comprehensive patient evaluation follows a systematic approach that relies heavily on the electronic health record, and (c) an easy-to-use display interface should have a simple layout that uses color and graphs to minimize time and energy spent reading it. When performing simulations using the low-fidelity prototype (phase 2), participants reported that (a) the machine learning predictions helped them to evaluate patient risk, (b) additional information about how to act on the risk estimate would be useful, and (c) correctable problems related to textual content existed. When performing simulations using the high-fidelity prototype (phase 3), usability problems predominantly related to the presentation of information and functionality. Despite the usability problems, participants rated the system highly on the System Usability Scale (mean score, 82.5; standard deviation, 10.5). CONCLUSIONS: Incorporating user needs and preferences into the design of a machine learning dashboard results in a display interface that clinicians rate as highly usable. Because the system demonstrates usability, evaluation of the effects of implementation on both process and clinical outcomes is warranted.
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Design Centrado no Usuário , Interface Usuário-Computador , Humanos , Grupos Focais , Registros Eletrônicos de Saúde , Complicações Pós-Operatórias/diagnóstico , Complicações Pós-Operatórias/etiologia , Complicações Pós-Operatórias/prevenção & controleRESUMO
Postoperative delirium (POD) has significant implications on morbidity, mortality, and health care expenditures. Monitoring electroencephalography (EEG) to adjust anesthetic management has gained interest as a strategy to mitigate POD. In this Pro-Con commentary article, the pro side supports the use of EEG to reduce POD, citing an empiric reduction in POD with processed EEG (pEEG)-guided general anesthesia found in several studies and recent meta-analysis. The Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES) trial is the exception to this, and issues with methods and achieved depths are discussed. Meanwhile, the Con side advocates that the use of EEG to reduce POD is not yet certain, citing that there is a lack of evidence that associations between anesthetic depth and POD represent causal relationships. The Con side also contends that the ideal EEG signatures to guide anesthetic titration are currently unknown, and the potential benefits of reduced anesthesia levels may be outweighed by the risks of potentially insufficient anesthetic administration. As the public health burden of POD increases, anesthesia clinicians will be tasked to consider interventions to mitigate risk such as EEG. This Pro-Con debate will provide 2 perspectives on the evidence and rationales for using EEG to mitigate POD.
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Anestesiologia , Anestésicos , Delírio do Despertar , Humanos , Idoso , Delírio do Despertar/diagnóstico , Delírio do Despertar/prevenção & controle , Anestesia Geral/efeitos adversos , EletroencefalografiaRESUMO
OBJECTIVES: Studies evaluating telemedicine critical care (TCC) have shown mixed results. We prospectively evaluated the impact of TCC implementation on risk-adjusted mortality among patients stratified by pre-TCC performance. DESIGN: Prospective, observational, before and after study. SETTING: Three adult ICUs at an academic medical center. PATIENTS: A total of 2,429 patients in the pre-TCC (January to June 2016) and 12,479 patients in the post-TCC (January 2017 to June 2019) periods. INTERVENTIONS: TCC implementation which included an acuity-driven workflow targeting an identified "lower-performing" patient group, defined by ICU admission in an Acute Physiology and Chronic Health Evaluation diagnoses category with a pre-TCC standardized mortality ratio (SMR) of greater than 1.5. MEASUREMENTS AND MAIN RESULTS: The primary outcome was risk-adjusted hospital mortality. Risk-adjusted hospital length of stay (HLOS) was also studied. The SMR for the overall ICU population was 0.83 pre-TCC and 0.75 post-TCC, with risk-adjusted mortalities of 10.7% and 9.5% (p = 0.09). In the identified lower-performing patient group, which accounted for 12.6% (n = 307) of pre-TCC and 13.3% (n = 1671) of post-TCC ICU patients, SMR decreased from 1.61 (95% CI, 1.21-2.01) pre-TCC to 1.03 (95% CI, 0.91-1.15) post-TCC, and risk-adjusted mortality decreased from 26.4% to 16.9% (p < 0.001). In the remaining ("higher-performing") patient group, there was no change in pre- versus post-TCC SMR (0.70 [0.59-0.81] vs 0.69 [0.64-0.73]) or risk-adjusted mortality (8.5% vs 8.4%, p = 0.86). There were no pre- to post-TCC differences in standardized HLOS ratio or risk-adjusted HLOS in the overall cohort or either performance group. CONCLUSIONS: In well-staffed and overall higher-performing ICUs in an academic medical center, Acute Physiology and Chronic Health Evaluation granularity allowed identification of a historically lower-performing patient group that experienced a striking TCC-associated reduction in SMR and risk-adjusted mortality. This study provides additional evidence for the relationship between pre-TCC performance and post-TCC improvement.
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Importance: Telemedicine for clinical decision support has been adopted in many health care settings, but its utility in improving intraoperative care has not been assessed. Objective: To pilot the implementation of a real-time intraoperative telemedicine decision support program and evaluate whether it reduces postoperative hypothermia and hyperglycemia as well as other quality of care measures. Design, Setting, and Participants: This single-center pilot randomized clinical trial (Anesthesiology Control Tower-Feedback Alerts to Supplement Treatments [ACTFAST-3]) was conducted from April 3, 2017, to June 30, 2019, at a large academic medical center in the US. A total of 26 254 adult surgical patients were randomized to receive either usual intraoperative care (control group; n = 12 980) or usual care augmented by telemedicine decision support (intervention group; n = 13 274). Data were initially analyzed from April 22 to May 19, 2021, with updates in November 2022 and February 2023. Intervention: Patients received either usual care (medical direction from the anesthesia care team) or intraoperative anesthesia care monitored and augmented by decision support from the Anesthesiology Control Tower (ACT), a real-time, live telemedicine intervention. The ACT incorporated remote monitoring of operating rooms by a team of anesthesia clinicians with customized analysis software. The ACT reviewed alerts and electronic health record data to inform recommendations to operating room clinicians. Main Outcomes and Measures: The primary outcomes were avoidance of postoperative hypothermia (defined as the proportion of patients with a final recorded intraoperative core temperature >36 °C) and hyperglycemia (defined as the proportion of patients with diabetes who had a blood glucose level ≤180 mg/dL on arrival to the postanesthesia recovery area). Secondary outcomes included intraoperative hypotension, temperature monitoring, timely antibiotic redosing, intraoperative glucose evaluation and management, neuromuscular blockade documentation, ventilator management, and volatile anesthetic overuse. Results: Among 26 254 participants, 13 393 (51.0%) were female and 20 169 (76.8%) were White, with a median (IQR) age of 60 (47-69) years. There was no treatment effect on avoidance of hyperglycemia (7445 of 8676 patients [85.8%] in the intervention group vs 7559 of 8815 [85.8%] in the control group; rate ratio [RR], 1.00; 95% CI, 0.99-1.01) or hypothermia (7602 of 11 447 patients [66.4%] in the intervention group vs 7783 of 11 672 [66.7.%] in the control group; RR, 1.00; 95% CI, 0.97-1.02). Intraoperative glucose measurement was more common among patients with diabetes in the intervention group (RR, 1.07; 95% CI, 1.01-1.15), but other secondary outcomes were not significantly different. Conclusions and Relevance: In this randomized clinical trial, anesthesia care quality measures did not differ between groups, with high confidence in the findings. These results suggest that the intervention did not affect the targeted care practices. Further streamlining of clinical decision support and workflows may help the intraoperative telemedicine program achieve improvement in targeted clinical measures. Trial Registration: ClinicalTrials.gov Identifier: NCT02830126.
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Hiperglicemia , Hipotermia , Adulto , Humanos , Feminino , Pessoa de Meia-Idade , Idoso , Masculino , Hipotermia/prevenção & controle , Hiperglicemia/prevenção & controle , Grupos Controle , Centros Médicos Acadêmicos , GlucoseRESUMO
To reduce Coronavirus Disease 2019 (COVID-19)-related mortality and morbidity, widely available oral COVID-19 treatments are urgently needed. Certain antidepressants, such as fluvoxamine or fluoxetine, may be beneficial against COVID-19. We included 388,945 adult inpatients who tested positive for SARS-CoV-2 at 36 AP−HP (Assistance Publique−Hôpitaux de Paris) hospitals from 2 May 2020 to 2 November 2021. We compared the prevalence of antidepressant use at admission in a 1:1 ratio matched analytic sample with and without COVID-19 (N = 82,586), and assessed its association with 28-day all-cause mortality in a 1:1 ratio matched analytic sample of COVID-19 inpatients with and without antidepressant use at admission (N = 1482). Antidepressant use was significantly less prevalent in inpatients with COVID-19 than in a matched control group of inpatients without COVID-19 (1.9% versus 4.8%; Odds Ratio (OR) = 0.38; 95%CI = 0.35−0.41, p < 0.001). Antidepressant use was significantly associated with reduced 28-day mortality among COVID-19 inpatients (12.8% versus 21.2%; OR = 0.55; 95%CI = 0.41−0.72, p < 0.001), particularly at daily doses of at least 40 mg fluoxetine equivalents. Antidepressants with high FIASMA (Functional Inhibitors of Acid Sphingomyelinase) activity seem to drive both associations. These treatments may reduce SARS-CoV-2 infections and COVID-19-related mortality in inpatients, and may be appropriate for prophylaxis and/or COVID-19 therapy for outpatients or inpatients.
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Antidepressants have previously been associated with better outcomes in patients hospitalized with COVID-19, but their effect on clinical deterioration among ambulatory patients has not been fully explored. The objective of this study was to assess whether antidepressant exposure was associated with reduced emergency department (ED) or hospital visits among ambulatory patients with SARS-CoV-2 infection. This retrospective cohort study included adult patients (N = 25 034) with a positive SARS-CoV-2 test performed in a non-hospital setting. Logistic regression analyses tested associations between home use of antidepressant medications and a composite outcome of ED visitation or hospital admission within 30 days. Secondary exposures included individual antidepressants and antidepressants with functional inhibition of acid sphingomyelinase (FIASMA) activity. Patients with antidepressant exposure were less likely to experience the primary composite outcome compared to patients without antidepressant exposure (adjusted odds ratio [aOR] 0.89, 95% CI 0.79-0.99, p = 0.04). This association was only observed with daily doses of at least 20 mg fluoxetine-equivalent (aOR 0.87, 95% CI 0.77-0.99, p = 0.04), but not with daily doses lower than 20 mg fluoxetine-equivalent (aOR 0.94, 95% CI 0.80-1.11, p = 0.48). In exploratory secondary analyses, the outcome incidence was also reduced with exposure to selective serotonin reuptake inhibitors (aOR 0.87, 95% CI 0.75-0.99, p = 0.04), bupropion (aOR 0.70, 95% CI 0.55-0.90, p = 0.005), and FIASMA antidepressant drugs (aOR 0.87, 95% CI 0.77-0.99, p = 0.03). Antidepressant exposure was associated with a reduced incidence of emergency department visitation or hospital admission among SARS-CoV-2 positive patients, in a dose-dependent manner. These data support the FIASMA model of antidepressants' effects against COVID-19.
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COVID-19 , SARS-CoV-2 , Adulto , Antidepressivos/farmacologia , Serviço Hospitalar de Emergência , Fluoxetina , Humanos , Pacientes Ambulatoriais , Estudos RetrospectivosRESUMO
Importance: Falls after elective inpatient surgical procedures are common and have physical, emotional, and financial consequences. Close interactions between patients and health care teams before and after surgical procedures may offer opportunities to address modifiable risk factors associated with falls. Objective: To assess whether a multicomponent intervention that incorporates education, home medication review, and home safety assessment is associated with reductions in the incidence of falls after elective inpatient surgical procedures. Design, Setting, and Participants: This prospective propensity score-matched cohort study was a prespecified secondary analysis of data from the Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES) randomized clinical trial, which was conducted at a single academic medical center between January 16, 2015, and May 7, 2018. Patients in the intervention group of the present study were enrolled in either arm of the ENGAGES clinical trial. Patients in the control group were selected from the Systematic Assessment and Targeted Improvement of Services Following Yearly Surgical Outcomes Surveys prospective observational cohort study, which created a registry of patient-reported postoperative outcomes at the same single center. The propensity score-matched cohort in the present study included 1396 patients (698 pairs) selected from a pool of 2013 eligible patients. All patients underwent elective surgical procedures with general anesthesia and had a hospital stay of 2 or more days. Data were analyzed from January 2, 2020, to January 11, 2022. Interventions: The multicomponent safety intervention (offered to all patients in the ENGAGES clinical trial) included patient education on fall prevention techniques, home medication review by a geriatric psychiatrist (with communication of recommended changes to the surgeon), a self-administered home safety assessment, and targeted occupational therapy home visits with home hazard removal (offered to patients with a preoperative history of falls). Main Outcomes and Measures: The primary outcome was patient-reported falls within 1 year after an elective inpatient surgical procedure. The secondary outcome was quality of life 1 year after an elective surgical procedure, which was measured using the physical and mental composite summary scores on the Veterans RAND 12-item health survey (score range, 0-100 points, with 0 indicating lowest quality of life and 100 indicating highest quality of life). Results: Among 1396 patients, the median age was 69 years (IQR, 64-75 years), and 739 patients (52.9%) were male. With regard to race, 5 patients (0.4%) were Asian, 97 (6.9%) were Black or African American, 2 (0.1%) were Native Hawaiian or Pacific Islander, 1237 (88.6%) were White, 3 (0.2%) were of other race, and 52 (3.7%) were of unknown race; with regard to ethnicity, 12 patients (0.9%) were Hispanic or Latino, 1335 (95.6%) were non-Hispanic or non-Latino, and 49 (3.5%) were of unknown ethnicity. Adherence to individual intervention components was modest (from 22.9% for completion of the self-administered home safety assessment to 28.2% for implementation of the geriatric psychiatrist's recommended medication changes). Falls within 1 year after surgical procedures were reported by 228 of 698 patients (32.7%) in the intervention group and 225 of 698 patients (32.2%) in the control group. No significant difference was found in falls between the 2 groups (standardized risk difference, 0.4%; 95% CI, -4.5% to 5.3%). After adjusting for preoperative quality of life, patients in the intervention group had higher physical composite summary scores (3.8 points; 95% CI, 2.4-5.1 points) and higher mental composite summary scores (5.7 points; 95% CI, 4.7-6.7 points) at 1 year compared with patients in the control group. Conclusions and Relevance: In this cohort study, a multicomponent safety intervention was not associated with reductions in falls within the first year after an elective surgical procedure; however, an increase in quality of life at 1 year was observed. These results suggest a need for other interventions, such as those designed to increase adherence, to lower the incidence of falls after surgical procedures.
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Procedimentos Cirúrgicos Eletivos , Qualidade de Vida , Acidentes por Quedas/prevenção & controle , Idoso , Estudos de Coortes , Procedimentos Cirúrgicos Eletivos/efeitos adversos , Feminino , Humanos , Pacientes Internados , Masculino , Pessoa de Meia-Idade , Estudos ProspectivosRESUMO
BACKGROUND: COVID-positive outpatients may benefit from remote monitoring, but such a program often relies on smartphone apps. This may introduce racial and socio-economic barriers to participation. Offering multiple methods for participation may address these barriers. OBJECTIVES: (1) To examine associations of race and neighborhood disadvantage with patient retention in a monitoring program offering two participation methods. (2) To measure the association of the program with emergency department visits and hospital admissions. DESIGN: Retrospective propensity-matched cohort study. PARTICIPANTS: COVID-positive outpatients at a single university-affiliated healthcare system and propensity-matched controls. INTERVENTIONS: A home monitoring program providing daily symptom tracking via patient portal app or telephone calls. MAIN MEASURES: Among program enrollees, retention (until 14 days, symptom resolution, or hospital admission) by race and neighborhood disadvantage, with stratification by program arm. In enrollees versus matched controls, emergency department utilization and hospital admission within 30 days. KEY RESULTS: There were 7592 enrolled patients and 9710 matched controls. Black enrollees chose the telephone arm more frequently than White enrollees (68% versus 44%, p = 0.009), as did those from more versus less disadvantaged neighborhoods (59% versus 43%, p = 0.02). Retention was similar in Black enrollees and White enrollees (63% versus 62%, p = 0.76) and in more versus less disadvantaged neighborhoods (63% versus 62%, p = 0.44). When stratified by program arm, Black enrollees had lower retention than White enrollees in the app arm (49% versus 55%, p = 0.01), but not in the telephone arm (69% versus 71%, p = 0.12). Compared to controls, enrollees more frequently visited the emergency department (HR 1.71 [95% CI 1.56-1.87]) and were admitted to the hospital (HR 1.16 [95% CI 1.02-1.31]). CONCLUSIONS: In a COVID-19 remote patient monitoring program, Black enrollees preferentially selected, and had higher retention in, telephone- over app-based monitoring. As a result, overall retention was similar between races. Remote monitoring programs with multiple modes may reduce barriers to participation.
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COVID-19 , COVID-19/epidemiologia , Estudos de Coortes , Humanos , Características da Vizinhança , Participação do Paciente , Estudos Retrospectivos , SARS-CoV-2RESUMO
Background: Postoperative depressive symptoms are associated with pain, readmissions, death, and other undesirable outcomes. Ketamine produces rapid but transient antidepressant effects in the perioperative setting. Longer infusions confer lasting antidepressant activity in patients with treatment-resistant depression, but it is unknown whether a similar approach may produce a lasting antidepressant effect after surgery. This protocol describes a pilot study that will assess the feasibility of conducting a larger scale randomized clinical trial addressing this knowledge gap. Methods: This single-center, double-blind, placebo-controlled pilot trial involves the enrollment of 32 patients aged 18 years or older with a history of depression scheduled for surgery with planned intensive care unit admission. On the first day following surgery and extubation, participants will be randomized to an intravenous eight-hour infusion of either ketamine (0.5 mg kg -1 over 10 minutes followed by a continuous rate of 0.3 mg kg -1 h -1) or an equal volume of normal saline. Depressive symptoms will be quantified using the Montgomery-Asberg Depression Rating Scale preoperatively and serially up to 14 days after the infusion. To detect ketamine-induced changes on overnight sleep architecture, a wireless headband will be used to record electroencephalograms preoperatively, during the study infusion, and after infusion. The primary feasibility endpoints will include the fraction of patients approached who enroll, the fraction of randomized patients who complete the study infusion, and the fraction of randomized patients who complete outcome data collection. Conclusions: This pilot study will evaluate the feasibility of a future large comparative effectiveness trial of ketamine to reduce depressive symptoms in postsurgical patients. Registration: K-PASS is registered on ClinicalTrials.gov: NCT05233566; registered February 10, 2022.
Assuntos
Ketamina , Humanos , Ketamina/uso terapêutico , Ketamina/efeitos adversos , Depressão/tratamento farmacológico , Estudos de Viabilidade , Projetos Piloto , Antidepressivos/uso terapêutico , Ensaios Clínicos Controlados Aleatórios como AssuntoRESUMO
Current approaches to understanding medication ordering errors rely on relatively small manually captured error samples. These approaches are resource-intensive, do not scale for computerized provider order entry (CPOE) systems, and are likely to miss important risk factors associated with medication ordering errors. Previously, we described a dataset of CPOE-based medication voiding accompanied by univariable and multivariable regression analyses. However, these traditional techniques require expert guidance and may perform poorly compared to newer approaches. In this paper, we update that analysis using machine learning (ML) models to predict erroneous medication orders and identify its contributing factors. We retrieved patient demographics (race/ethnicity, sex, age), clinician characteristics, type of medication order (inpatient, prescription, home medication by history), and order content. We compared logistic regression, random forest, boosted decision trees, and artificial neural network models. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). The dataset included 5,804,192 medication orders, of which 28,695 (0.5%) were voided. ML correctly classified voids at reasonable accuracy; with a positive predictive value of 10%, ~20% of errors were included. Gradient boosted decision trees achieved the highest AUROC (0.7968) and AUPRC (0.0647) among all models. Logistic regression had the poorest performance. Models identified predictive factors with high face validity (e.g., student orders), and a decision tree revealed interacting contexts with high rates of errors not identified by previous regression models. Prediction models using order-entry information offers promise for error surveillance, patient safety improvements, and targeted clinical review. The improved performance of models with complex interactions points to the importance of contextual medication ordering information for understanding contributors to medication errors.
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Aprendizado de Máquina , Erros de Medicação , Humanos , Sistemas de Registro de Ordens Médicas , Segurança do PacienteRESUMO
BACKGROUND: Intraoperative EEG suppression duration has been associated with postoperative delirium and mortality. In a clinical trial testing anaesthesia titration to avoid EEG suppression, the intervention did not decrease the incidence of postoperative delirium, but was associated with reduced 30-day mortality. The present study evaluated whether the EEG-guided anaesthesia intervention was also associated with reduced 1-yr mortality. METHODS: This manuscript reports 1 yr follow-up of subjects from a single-centre RCT, including a post hoc secondary outcome (1-yr mortality) in addition to pre-specified secondary outcomes. The trial included subjects aged 60 yr or older undergoing surgery with general anaesthesia between January 2015 and May 2018. Patients were randomised to receive EEG-guided anaesthesia or usual care. The previously reported primary outcome was postoperative delirium. The outcome of the current study was all-cause 1-yr mortality. RESULTS: Of the 1232 subjects enrolled, 614 subjects were randomised to EEG-guided anaesthesia and 618 subjects to usual care. One-year mortality was 57/591 (9.6%) in the guided group and 62/601 (10.3%) in the usual-care group. No significant difference in mortality was observed (adjusted absolute risk difference, -0.7%; 99.5% confidence interval, -5.8% to 4.3%; P=0.68). CONCLUSIONS: An EEG-guided anaesthesia intervention aiming to decrease duration of EEG suppression during surgery did not significantly decrease 1-yr mortality. These findings, in the context of other studies, do not provide supportive evidence for EEG-guided anaesthesia to prevent intermediate term postoperative death. CLINICAL TRIAL REGISTRATION: NCT02241655.
Assuntos
Anestesia/mortalidade , Eletroencefalografia , Monitorização Neurofisiológica Intraoperatória , Complicações Pós-Operatórias/mortalidade , Acidentes por Quedas , Idoso , Anestesia/efeitos adversos , Monitores de Consciência , Delírio/etiologia , Delírio/mortalidade , Eletroencefalografia/instrumentação , Feminino , Humanos , Monitorização Neurofisiológica Intraoperatória/instrumentação , Masculino , Pessoa de Meia-Idade , Missouri , Complicações Cognitivas Pós-Operatórias/etiologia , Complicações Cognitivas Pós-Operatórias/mortalidade , Complicações Pós-Operatórias/etiologia , Valor Preditivo dos Testes , Qualidade de Vida , Medição de Risco , Fatores de Risco , Fatores de Tempo , Resultado do TratamentoRESUMO
INTRODUCTION: Delirium is a potentially preventable disorder characterised by acute disturbances in attention and cognition with fluctuating severity. Postoperative delirium is associated with prolonged intensive care unit and hospital stay, cognitive decline and mortality. The development of biomarkers for tracking delirium could potentially aid in the early detection, mitigation and assessment of response to interventions. Because sleep disruption has been posited as a contributor to the development of this syndrome, expression of abnormal electroencephalography (EEG) patterns during sleep and wakefulness may be informative. Here we hypothesise that abnormal EEG patterns of sleep and wakefulness may serve as predictive and diagnostic markers for postoperative delirium. Such abnormal EEG patterns would mechanistically link disrupted thalamocortical connectivity to this important clinical syndrome. METHODS AND ANALYSIS: P-DROWS-E (Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography) is a 220-patient prospective observational study. Patient eligibility criteria include those who are English-speaking, age 60 years or older and undergoing elective cardiac surgery requiring cardiopulmonary bypass. EEG acquisition will occur 1-2 nights preoperatively, intraoperatively, and up to 7 days postoperatively. Concurrent with EEG recordings, two times per day postoperative Confusion Assessment Method (CAM) evaluations will quantify the presence and severity of delirium. EEG slow wave activity, sleep spindle density and peak frequency of the posterior dominant rhythm will be quantified. Linear mixed-effects models will be used to evaluate the relationships between delirium severity/duration and EEG measures as a function of time. ETHICS AND DISSEMINATION: P-DROWS-E is approved by the ethics board at Washington University in St. Louis. Recruitment began in October 2018. Dissemination plans include presentations at scientific conferences, scientific publications and mass media. TRIAL REGISTRATION NUMBER: NCT03291626.