Pulse contour techniques for perioperative hemodynamic monitoring: A nationwide carbon footprint and cost estimation.

BACKGROUND: The question of environmentally sustainable perioperative medicine represents a new challenge in an era of cost constraints and climate crisis. The French Society of Anaesthesia and Intensive Care (SFAR) recommends stroke volume optimization in high-risk surgical patients. Pulse contour techniques have become increasingly popular for stroke volume monitoring during surgery. Some require the use of specific disposable pressure transducers (DPTs), whereas others can be used with standard DPTs. OBJECTIVE: Quantify and compare the carbon footprint and cost of pulse contour techniques using specific and standard DPTs on a yearly basis and at a national level. METHODS: We estimated the number of high-risk surgical patients monitored every year in France with a pulse contour technique, and the plastic waste, carbon footprint and cost associated with the use of specific and standard DPTs. MAIN FINDINGS: When compared to pulse contour techniques working with a standard DPT, techniques requiring a specific DPT are responsible for an increase in carbon dioxide emission estimated at 65-83 tons/yr and for additional hospital cost estimated at €67 million/yr. If, as recommended by the SFAR, all high-risk surgical patients were monitored, the difference would reach 179-227 tons/yr for the environmental impact and €187 million/yr for the economic impact. CONCLUSION: From an environmental and economic standpoint, pulse contour techniques working with standard DPTs should be recommended for the perioperative hemodynamic monitoring of high-risk surgical patients.

Assessment of Machine Learning to Estimate the Individual Treatment Effect of Corticosteroids in Septic Shock.

Importance: The survival benefit of corticosteroids in septic shock remains uncertain. Objective: To estimate the individual treatment effect (ITE) of corticosteroids in adults with septic shock in intensive care units using machine learning and to evaluate the net benefit of corticosteroids when the decision to treat is based on the individual estimated absolute treatment effect. Design, Setting, and Participants: This cohort study used individual patient data from 4 trials on steroid supplementation in adults with septic shock as a training cohort to model the ITE using an ensemble machine learning approach. Data from a double-blinded, placebo-controlled randomized clinical trial comparing hydrocortisone with placebo were used for external validation. Data analysis was conducted from September 2019 to February 2020. Exposures: Intravenous hydrocortisone 50 mg dose every 6 hours for 5 to 7 days with or without enteral 50 µg of fludrocortisone daily for 7 days. The control was either the placebo or usual care. Main Outcomes and Measures: All-cause 90-day mortality. Results: A total of 2548 participants were included in the development cohort, with median (interquartile range [IQR]) age of 66 (55-76) years and 1656 (65.0%) men. The median (IQR) Simplified Acute Physiology Score (SAPS II) was 55 [42-69], and median (IQR) Sepsis-related Organ Failure Assessment score on day 1 was 11 (9-13). The crude pooled relative risk (RR) of death at 90 days was 0.89 (95% CI, 0.83 to 0.96) in favor of corticosteroids. According to the optimal individual model, the estimated median absolute risk reduction was of 2.90% (95% CI, 2.79% to 3.01%). In the external validation cohort of 75 patients, the area under the curve of the optimal individual model was 0.77 (95% CI, 0.59 to 0.92). For any number willing to treat (NWT; defined as the acceptable number of people to treat to avoid 1 additional outcome considering the risk of harm associated with the treatment) less than 25, the net benefit of treating all patients vs treating nobody was negative. When the NWT was 25, the net benefit was 0.01 for the treat all with hydrocortisone strategy, -0.01 for treat all with hydrocortisone and fludrocortisone strategy, 0.06 for the treat by SAPS II strategy, and 0.31 for the treat by optimal individual model strategy. The net benefit of the SAPS II and the optimal individual model treatment strategies converged to zero for a smaller number willing to treat, but the individual model was consistently superior than model based on the SAPS II score. Conclusions and Relevance: These findings suggest that an individualized treatment strategy to decide which patient with septic shock to treat with corticosteroids yielded positive net benefit regardless of potential corticosteroid-associated side effects.

Concordance of common data elements for assessment of subjective cognitive complaints after mild-traumatic brain injury: a TRACK-TBI Pilot Study.

OBJECTIVE: To determine characteristics and concordance of subjective cognitive complaints (SCCs) 6 months following mild-traumatic brain injury (mTBI) as assessed by two different TBI common data elements (CDEs). RESEARCH DESIGN: The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot Study was a prospective observational study that utilized the NIH TBI CDEs, Version 1.0. We examined variables associated with SCC, performance on objective cognitive tests (Wechsler Adult Intelligence Scale, California Verbal Learning Test, and Trail Making Tests A and B), and agreement on self-report of SCCs as assessed by the acute concussion evaluation (ACE) versus the Rivermead Post Concussion Symptoms Questionnaire (RPQ). RESULTS: In total, 68% of 227 participants endorsed SCCs at 6 months. Factors associated with SCC included less education, psychiatric history, and being assaulted. Compared to participants without SCC, those with SCC defined by RPQ performed significantly worse on all cognitive tests. There was moderate agreement between the two measures of SCCs (kappa = 0.567 to 0.680). CONCLUSION: We show that the symptom questionnaires ACE and RPQ show good, but not excellent, agreement for SCCs in an mTBI study population. Our results support the retention of RPQ as a basic CDE for mTBI research. ABBREVIATIONS: BSI-18: Brief Symptom Inventory; 18CDEs: common data elements; CT: computed tomography; CVLT: California Verbal Learning Test; ED: emergency department; GCS: Glasgow coma scale; LOC: loss of consciousnessm; TBI: mild-traumatic brain injury; PTA: post-traumatic amnesia; SCC: subjective cognitive complaints; TBI: traumatic brain injury; TRACK-TBI: Transforming Research and Clinical Knowledge in Traumatic Brain Injury; TMT: Trail Making Test; WAIS-PSI: Wechsler Adult Intelligence Scale, Fourth Edition, Processing Speed Index.

A new weighted balance measure helped to select the variables to be included in a propensity score model.

OBJECTIVES: The propensity score (PS) is a balancing score. Following PS matching, balance checking usually relies on estimating separately the standardized absolute mean difference for each baseline characteristic. The average standardized absolute mean difference and the Mahalanobis distances have been proposed to summarize the information across the covariates. However, they might be minimized when nondesirable variables such as instrumental variables (IV) are included in the PS model. We propose a new weighted summary balance measure that takes into account, for each covariate, its strength of association with the outcome. STUDY DESIGN AND SETTING: This new measure was evaluated using a simulation study to assess whether minimization of the measure coincided with minimally biased estimates. All measures were then applied to a real data set from an observational cohort study. RESULTS: Contrarily to the other measures, our proposal was minimized when including the confounders, which coincided with minimal bias and mean squared error, but increased when including an IV in the PS model. Similar findings were observed in the real data set. CONCLUSION: A balance measure taking into account the strength of association between the covariates and the outcome may be helpful to identify the most parsimonious PS model.

Evaluation of the propensity score methods for estimating marginal odds ratios in case of small sample size.

BACKGROUND: Propensity score (PS) methods are increasingly used, even when sample sizes are small or treatments are seldom used. However, the relative performance of the two mainly recommended PS methods, namely PS-matching or inverse probability of treatment weighting (IPTW), have not been studied in the context of small sample sizes. METHODS: We conducted a series of Monte Carlo simulations to evaluate the influence of sample size, prevalence of treatment exposure, and strength of the association between the variables and the outcome and/or the treatment exposure, on the performance of these two methods. RESULTS: Decreasing the sample size from 1,000 to 40 subjects did not substantially alter the Type I error rate, and led to relative biases below 10%. The IPTW method performed better than the PS-matching down to 60 subjects. When N was set at 40, the PS matching estimators were either similarly or even less biased than the IPTW estimators. Including variables unrelated to the exposure but related to the outcome in the PS model decreased the bias and the variance as compared to models omitting such variables. Excluding the true confounder from the PS model resulted, whatever the method used, in a significantly biased estimation of treatment effect. These results were illustrated in a real dataset. CONCLUSION: Even in case of small study samples or low prevalence of treatment, PS-matching and IPTW can yield correct estimations of treatment effect unless the true confounders and the variables related only to the outcome are not included in the PS model.

Implications of ICU triage decisions on patient mortality: a cost-effectiveness analysis.

INTRODUCTION: Intensive care is generally regarded as expensive, and as a result beds are limited. This has raised serious questions about rationing when there are insufficient beds for all those referred. However, the evidence for the cost effectiveness of intensive care is weak and the work that does exist usually assumes that those who are not admitted do not survive, which is not always the case. Randomised studies of the effectiveness of intensive care are difficult to justify on ethical grounds; therefore, this observational study examined the cost effectiveness of ICU admission by comparing patients who were accepted into ICU after ICU triage to those who were not accepted, while attempting to adjust such comparison for confounding factors. METHODS: This multi-centre observational cohort study involved 11 hospitals in 7 EU countries and was designed to assess the cost effectiveness of admission to intensive care after ICU triage. A total of 7,659 consecutive patients referred to the intensive care unit (ICU) were divided into those accepted for admission and those not accepted. The two groups were compared in terms of cost and mortality using multilevel regression models to account for differences across centres, and after adjusting for age, Karnofsky score and indication for ICU admission. The analyses were also stratified by categories of Simplified Acute Physiology Score (SAPS) II predicted mortality (< 5%, 5% to 40% and >40%). Cost effectiveness was evaluated as cost per life saved and cost per life-year saved. RESULTS: Admission to ICU produced a relative reduction in mortality risk, expressed as odds ratio, of 0.70 (0.52 to 0.94) at 28 days. When stratified by predicted mortality, the odds ratio was 1.49 (0.79 to 2.81), 0.7 (0.51 to 0.97) and 0.55 (0.37 to 0.83) for <5%, 5% to 40% and >40% predicted mortality, respectively. Average cost per life saved for all patients was $103,771 (€82,358) and cost per life-year saved was $7,065 (€5,607). These figures decreased substantially for patients with predicted mortality higher than 40%, $60,046 (€47,656) and $4,088 (€3,244), respectively. Results were very similar when considering three-month mortality. Sensitivity analyses performed to assess the robustness of the results provided findings similar to the main analyses. CONCLUSIONS: Not only does ICU appear to produce an improvement in survival, but the cost per life saved falls for patients with greater severity of illness. This suggests that intensive care is similarly cost effective to other therapies that are generally regarded as essential.