A Familiar Working Environment Influences Surgeon's Stress in the Operating Room: A Multi-Specialty Prospective Cohort Study.

OBJECTIVE: To determine the influence of operating room familiarity on surgeon stress. BACKGROUND: Regulating surgeon stress may improve patient safety. This study evaluated how assisting surgeon and operating room familiarity influence intraoperative heart rate variability among surgeons. METHODS: Attending surgeons from seven specialties within four university hospitals in France were enrolled from 11/01/20-12/31/21. Vagal tone, an indicator of stress derived from heart rate variability, was assessed during the first five minutes after incision using the root mean square of successive differences (RMSSD). Higher RMSSD values indicate greater vagal tone. Team familiarity was quantified as the cumulative time the attending and assisting surgeons had operated together in the past, while operating rooms in which the surgeon conducted >10% of their operations were termed familiar. The effect of each on the RMSSD was assessed via a linear mixed-effect model adjusting for the random effect of the surgeon and possible confounders. RESULTS: Overall, 643 surgeries performed by 37 surgeons were included. Median surgeon age was 49 years, 29(78.4%) were male, and 22(59.5%) were professors. Surgeons spent an average of 21.2 hours with the assisting surgeon prior to surgery and conducted 585(91.0%) of their operations in a familiar operating room. For every 10 additional hours spent operating together, ln(RMSSD) significantly increased by 0.018 (95%CI: 0.003 to 0.033, P=0.016). Familiar operating rooms also tended to increase surgeon ln(RMSSD) [0.098 (95%CI: -0.007 to 0.203, P=0.068)]. CONCLUSION: Familiar assisting surgeons, and potentially operating rooms, increased surgeon vagal tone. Maintaining a stable operating room environment may improve surgeon stress and patient care.

Combining the Hospital Frailty Risk Score With the Charlson and Elixhauser Multimorbidity Indices to Identify Older Patients at Risk of Poor Outcomes in Acute Care.

OBJECTIVE: The Hospital Frailty Risk Score (HFRS) can be applied to medico-administrative datasets to determine the risks of 30-day mortality and long length of stay (LOS) in hospitalized older patients. The objective of this study was to compare the HFRS with Charlson and Elixhauser comorbidity indices, used separately or combined. DESIGN: A retrospective analysis of the French medical information database. The HFRS, Charlson index, and Elixhauser index were calculated for each patient based on the index stay and hospitalizations over the preceding 2 years. Different constructions of the HFRS were considered based on overlapping diagnostic codes with either Charlson or Elixhauser indices. We used mixed logistic regression models to investigate the association between outcomes, different constructions of HFRS, and associations with comorbidity indices. SETTING: 743 hospitals in France. PARTICIPANTS: All patients aged 75 years or older hospitalized as an emergency in 2017 (n=1,042,234).Main outcome measures: 30-day inpatient mortality and LOS >10 days. RESULTS: The HFRS, Charlson, and Elixhauser indices were comparably associated with an increased risk of 30-day inpatient mortality and long LOS. The combined model with the highest c-statistic was obtained when associating the HFRS with standard adjustment and Charlson for 30-day inpatient mortality (adjusted c-statistics: HFRS=0.654; HFRS + Charlson = 0.676) and with Elixhauser for long LOS (adjusted c-statistics: HFRS= 0.672; HFRS + Elixhauser =0.698). CONCLUSIONS: Combining comorbidity indices and HFRS may improve discrimination for predicting long LOS in hospitalized older people, but adds little to Charlson's 30-day inpatient mortality risk.

Length of Stay Prediction With Standardized Hospital Data From Acute and Emergency Care Using a Deep Neural Network.

OBJECTIVE: Length of stay (LOS) is an important metric for the organization and scheduling of care activities. This study sought to propose a LOS prediction method based on deep learning using widely available administrative data from acute and emergency care and compare it with other methods. PATIENTS AND METHODS: All admissions between January 1, 2011 and December 31, 2019, at 6 university hospitals of the Hospices Civils de Lyon metropolis were included, leading to a cohort of 1,140,100 stays of 515,199 patients. Data included demographics, primary and associated diagnoses, medical procedures, the medical unit, the admission type, socio-economic factors, and temporal information. A model based on embeddings and a Feed-Forward Neural Network (FFNN) was developed to provide fine-grained LOS predictions per hospitalization step. Performances were compared with random forest and logistic regression, with the accuracy, Cohen kappa, and a Bland-Altman plot, through a 5-fold cross-validation. RESULTS: The FFNN achieved an accuracy of 0.944 (CI: 0.937, 0.950) and a kappa of 0.943 (CI: 0.935, 0.950). For the same metrics, random forest yielded 0.574 (CI: 0.573, 0.575) and 0.602 (CI: 0.601, 0.603), respectively, and 0.352 (CI: 0.346, 0.358) and 0.414 (CI: 0.408, 0.422) for the logistic regression. The FFNN had a limit of agreement ranging from -2.73 to 2.67, which was better than random forest (-6.72 to 6.83) or logistic regression (-7.60 to 9.20). CONCLUSION: The FFNN was better at predicting LOS than random forest or logistic regression. Implementing the FFNN model for routine acute care could be useful for improving the quality of patients' care.

Long-Term Safety of Teriflunomide in Multiple Sclerosis Patients: Results of Prospective Comparative Studies in Three European Countries.

BACKGROUND AND OBJECTIVES: Teriflunomide is a disease-modifying therapy (DMT) for multiple sclerosis (MS). This post authorisation safety study assessed risks of adverse events of special interest (AESI) associated with teriflunomide use. METHODS: Secondary use of individual data from the Danish MS Registry (DMSR), the French National Health Data System (SNDS), the Belgian national database of health care claims (AIM-IMA) and the Belgian Treatments in MS Registry (Beltrims). We included patients treated with a DMT at the date of teriflunomide reimbursement or initiating another DMT. Adjusted hazard rates (aHR) and 95% confidence intervals were derived from Cox models with time-dependent exposure comparing teriflunomide treatment with another DMT. RESULTS: Of 81 620 patients (72% women) included in the cohort, 22 324 (27%) were treated with teriflunomide. After a median follow-up of 4 years, teriflunomide use compared to other DMT was not associated with a risk of all-cause mortality, severe infection, pneumoniae, herpes zoster reactivation, pancreatitis, cardiovascular condition and cancers. For opportunistic infections, aHR for teriflunomide versus other DMT was 2.4 (1.2-4.8) in SNDS, which was not bound to a particular opportunistic agent. The aHR was 2.0 (1.1-3.7) for renal failures in the SNDS, but no association was found in other data sources. A total of 187 SNDS patients had a history of renal failure prior to cohort entry. None of these patients (0%) had a renal failure recurrence when treated with teriflunomide for 19 (13%) recurrences reported for patients on another DMT. DISCUSSION: We found no evidence that teriflunomide use would be associated with an increased risk of AESI. Trial Registration EUPAS register: EU PAS 19610.

Nurse-to-Nurse Familiarity and Mortality in the Critically Ill: A Multicenter Observational Study.

Rationale: Nurse-to-nurse familiarity at work should strengthen the components of teamwork and enhance its efficiency. However, its impact on patient outcomes in critical care remains poorly investigated. Objectives: To explore the role of nurse-to-nurse familiarity on inpatient deaths during ICU stay. Methods: This was a retrospective observational study in eight adult academic ICUs between January 1, 2011 and December 31, 2016. Measurements and Main Results: Nurse-to-nurse familiarity was measured across day and night 12-hour daily shifts as the mean number of previous collaborations between each nursing team member during previous shifts within the given ICU (suboptimal if <50). Primary outcome was a shift with at least one inpatient death, excluding death of patients with a decision to forego life-sustaining therapy. A multiple modified Poisson regression was computed to identify the determinants of mortality per shift, taking into account ICU, patient characteristics, patient-to-nurse and patient-to-assistant nurse ratios, nurse experience length, and workload. A total of 43,479 patients were admitted, of whom 3,311 (8%) died. The adjusted model showed a lower risk of a shift with mortality when nurse-to-nurse familiarity increased in the shift (relative risk, 0.90; 95% confidence interval per 10 shifts, 0.82-0.98; P = 0.012). Low nurse-to-nurse familiarity during the shift combined with suboptimal patient-to-nurse and patient-to-assistant nurse ratios (suboptimal if >2.5 and >4, respectively) were associated with increased risk of shift with mortality (relative risk, 1.84; 95% confidence interval, 1.15-2.96; P < 0.001). Conclusions: Shifts with low nurse-to-nurse familiarity were associated with an increased risk of patient deaths.

The impact of hospital saturation on non-COVID-19 hospital mortality during the pandemic in France: a national population-based cohort study.

BACKGROUND: A previous study reported significant excess mortality among non-COVID-19 patients due to disrupted surgical care caused by resource prioritization for COVID-19 cases in France. The primary objective was to investigate if a similar impact occurred for medical conditions and determine the effect of hospital saturation on non-COVID-19 hospital mortality during the first year of the pandemic in France. METHODS: We conducted a nationwide population-based cohort study including all adult patients hospitalized for non-COVID-19 acute medical conditions in France between March 1, 2020 and 31 May, 2020 (1st wave) and September 1, 2020 and December 31, 2020 (2nd wave). Hospital saturation was categorized into four levels based on weekly bed occupancy for COVID-19: no saturation (< 5%), low saturation (> 5% and ≤ 15%), moderate saturation (> 15% and ≤ 30%), and high saturation (> 30%). Multivariate generalized linear model analyzed the association between hospital saturation and mortality with adjustment for age, sex, COVID-19 wave, Charlson Comorbidity Index, case-mix, source of hospital admission, ICU admission, category of hospital and region of residence. RESULTS: A total of 2,264,871 adult patients were hospitalized for acute medical conditions. In the multivariate analysis, the hospital mortality was significantly higher in low saturated hospitals (adjusted Odds Ratio/aOR = 1.05, 95% CI [1.34-1.07], P < .001), moderate saturated hospitals (aOR = 1.12, 95% CI [1.09-1.14], P < .001), and highly saturated hospitals (aOR = 1.25, 95% CI [1.21-1.30], P < .001) compared to non-saturated hospitals. The proportion of deaths outside ICU was higher in highly saturated hospitals (87%) compared to non-, low- or moderate saturated hospitals (81-84%). The negative impact of hospital saturation on mortality was more pronounced in patients older than 65 years, those with fewer comorbidities (Charlson 1-2 and 3 vs. 0), patients with cancer, nervous and mental diseases, those admitted from home or through the emergency room (compared to transfers from other hospital wards), and those not admitted to the intensive care unit. CONCLUSIONS: Our study reveals a noteworthy "dose-effect" relationship: as hospital saturation intensifies, the non-COVID-19 hospital mortality risk also increases. These results raise concerns regarding hospitals' resilience and patient safety, underscoring the importance of identifying targeted strategies to enhance resilience for the future, particularly for high-risk patients.

Impact of the COVID-19 pandemic on non-COVID-19 hospital mortality in patients with schizophrenia: a nationwide population-based cohort study.

It remains unknown to what degree resource prioritization toward SARS-CoV-2 (2019-nCoV) coronavirus (COVID-19) cases had disrupted usual acute care for non-COVID-19 patients, especially in the most vulnerable populations such as patients with schizophrenia. The objective was to establish whether the impact of the COVID-19 pandemic on non-COVID-19 hospital mortality and access to hospital care differed between patients with schizophrenia versus without severe mental disorder. We conducted a nationwide population-based cohort study of all non-COVID-19 acute hospitalizations in the pre-COVID-19 (March 1, 2019 through December 31, 2019) and COVID-19 (March 1, 2020 through December 31, 2020) periods in France. We divided the population into patients with schizophrenia and age/sex-matched patients without severe mental disorder (1:10). Using a difference-in-differences approach, we performed multivariate patient-level logistic regression models (adjusted odds ratio, aOR) with adjustment for complementary health insurance, smoking, alcohol and substance addiction, Charlson comorbidity score, origin of the patient, category of care, intensive care unit (ICU) care, major diagnosis groups and hospital characteristics. A total of 198,186 patients with schizophrenia were matched with 1,981,860 controls. The 90-day hospital mortality in patients with schizophrenia increased significantly more versus controls (aOR = 1.18; p < 0.001). This increased mortality was found for poisoning and injury (aOR = 1.26; p = 0.033), respiratory diseases (aOR = 1.19; p = 0.008) and for both surgery (aOR = 1.26; p = 0.008) and medical care settings (aOR = 1.16; p = 0.001). Significant changes in the case mix were noted with reduced admission in the ICU and for several somatic diseases including cancer, circulatory and digestive diseases and stroke for patients with schizophrenia compared to controls. These results suggest a greater deterioration in access to, effectiveness and safety of non-COVID-19 acute care in patients with schizophrenia compared to patients without severe mental disorders. These findings question hospitals' resilience pertaining to patient safety and underline the importance of developing specific strategies for vulnerable patients in anticipation of future public health emergencies.

The Economic Cost of Thyroid Cancer in France and the Corresponding Share Associated With Treatment of Overdiagnosed Cases.

OBJECTIVES: Thyroid cancer incidence in France has increased rapidly in recent decades. Most of this increase has been attributed to overdiagnosis, the major consequence of which is overtreatment. We aimed to estimate the cost of thyroid cancer management in France and the corresponding cost proportion attributable to the treatment of overdiagnosed cases. METHODS: Multiple data sources were integrated: the mean cost per patient with thyroid cancer was estimated by using the Echantillon Généraliste des Bénéficiaires data set; thyroid cancer cases attributable to overdiagnosis were estimated for 21 departments using data from the French network of cancer registries and extrapolated to the whole country; medical records from 6 departments were used to refine the diagnosis and care pathway. RESULTS: Between 2011 and 2015, 33 911 women and 10 846 men in France were estimated to be diagnosed of thyroid cancer, with mean cost per capita of €6248. Among those treated, 8114 to 14 925 women and 1465 to 3626 men were due to overdiagnosis. The total cost of thyroid cancer patient management was €203.5 million (€154.3 million for women and €49.3 million for men), of which between €59.9 million (or 29.4% of the total cost, lower bound) and €115.9 million (or 56.9% of the total cost, upper bound) attributable to treatment of overdiagnosed cases. CONCLUSIONS: The management of thyroid cancer represents not only a relevant clinical and public health problem in France but also a potentially important economic burden. Overdiagnosis and corresponding associated treatments play an important role on the total costs of thyroid cancer management.

Mortality Among Noncoronavirus Disease 2019 Critically Ill Patients Attributable to the Pandemic in France.

OBJECTIVES: We investigated whether the risk of death among noncoronavirus disease 2019 critically ill patients increased when numerous coronavirus disease 2019 cases were admitted concomitantly to the same hospital units. DESIGN: We performed a nationwide observational study based on the medical information system from all public and private hospitals in France. SETTING: Information pertaining to every adult admitted to ICUs or intermediate care units from 641 hospitals between January 1, 2020, and June 30, 2020 was analyzed. PATIENTS: A total of 454,502 patients (428,687 noncoronavirus disease 2019 and 25,815 coronavirus disease 2019 patients) were included. INTERVENTIONS: For each noncoronavirus disease 2019 patient, pandemic exposure during their stay was calculated per day using the proportion of coronavirus disease 2019 patients among all patients treated in ICU. MEASUREMENTS AND MAIN RESULTS: We computed a multivariable logistic regression model to estimate the influence of pandemic exposure (low, moderate, and high exposure) on noncoronavirus disease 2019 patient mortality during ICU stay. We adjusted on patient and hospital confounders. The risk of death among noncoronavirus disease 2019 critically ill patients increased in case of moderate (adjusted odds ratio, 1.12; 95% CI, 1.05-1.19; p < 0.001) and high pandemic exposures (1.52; 95% CI, 1.33-1.74; p < 0.001). CONCLUSIONS: In hospital units with moderate or high levels of coronavirus disease 2019 critically ill patients, noncoronavirus disease deaths were at higher levels.

External validation of the Hospital Frailty Risk Score in France.

BACKGROUND: The Hospital Frailty Risk Score (HFRS) has made it possible internationally to identify subgroups of patients with characteristics of frailty from routinely collected hospital data. OBJECTIVE: To externally validate the HFRS in France. DESIGN: A retrospective analysis of the French medical information database. SETTING: 743 hospitals in Metropolitan France. SUBJECTS: All patients aged 75 years or older hospitalised as an emergency in 2017 (n = 1,042,234). METHODS: The HFRS was calculated for each patient based on the index stay and hospitalisations over the preceding 2 years. Main outcome measures were 30-day in-patient mortality, length of stay (LOS) >10 days and 30-day readmissions. Mixed logistic regression models were used to investigate the association between outcomes and HFRS score. RESULTS: Patients with high HFRS risk were associated with increased risk of mortality and prolonged LOS (adjusted odds ratio [aOR] = 1.38 [1.35-1.42] and 3.27 [3.22-3.32], c-statistics = 0.676 and 0.684, respectively), while it appeared less predictive of readmissions (aOR = 1.00 [0.98-1.02], c-statistic = 0.600). Model calibration was excellent. Restricting the score to data prior to index admission reduced discrimination of HFRS substantially. CONCLUSIONS: HFRS can be used in France to determine risks of 30-day in-patient mortality and prolonged LOS, but not 30-day readmissions. Trial registration: Reference ID on clinicaltrials.gov: ID: NCT03905629.

Effects of relaxing breathing paired with cardiac biofeedback on performance and relaxation during critical simulated situations: a prospective randomized controlled trial.

BACKGROUND: Active participation in high-fidelity simulation remains stressful for residents. Increased stress levels elicited during such simulation impacts performance. We tested whether relaxing breathing, paired or not with cardiac biofeedback, could lead to enhanced performance of residents during simulation. METHODS: This randomized pilot study involved the fifth-year anesthesiology and critical care residents who participated in high-fidelity at Lyon medical simulation center in 2019. Residents were randomized into three parallel interventions: relaxing breathing, relaxing breathing paired with cardiac biofeedback, and control. Each intervention was applied for five minutes immediately after the scenario briefing. The primary endpoint was the overall performance during the simulation rated by two blinded independent investigators. The secondary endpoints included component scores of overall performance and changes in psychological states. RESULTS: Thirty-four residents were included. Compared to the control group, residents in the relaxing breathing (+ 7%, 98.3% CI: 0.3 to 13.7, P = 0.013) and relaxing breathing paired with cardiac biofeedback (+ 8%, 98.3% CI: 0.82 to 14.81, P = 0.009) groups had a higher overall performance score. Following the interventions, compared to the control group, stress level was lower when participants had performed relaxing breathing alone (P = 0.029) or paired with biofeedback (P = 0.035). The internal relaxation level was higher in both the relaxing breathing alone (P = 0.016) and paired with biofeedback groups (P = 0.035). CONCLUSIONS: Performing five minutes of relaxing breathing before the scenario resulted in better overall simulation performance. These preliminary findings suggest that short breathing interventions are effective in improving performance during simulation. TRIAL REGISTRATION: The study protocol was retrospectively registered on clinicaltrials.gov ( NCT04141124 , 28/10/2019).

Hospital Length of Stay Prediction Methods: A Systematic Review.

OBJECTIVE: This systematic review sought to establish a picture of length of stay (LOS) prediction methods based on available hospital data and study protocols designed to measure their performance. MATERIALS AND METHODS: An English literature search was done relative to hospital LOS prediction from 1972 to September 2019 according to the PRISMA guidelines. Articles were retrieved from PubMed, ScienceDirect, and arXiv databases. Information were extracted from the included papers according to a standardized assessment of population setting and study sample, data sources and input variables, LOS prediction methods, validation study design, and performance evaluation metrics. RESULTS: Among 74 selected articles, 98.6% (73/74) used patients' data to predict LOS; 27.0% (20/74) used temporal data; and 21.6% (16/74) used the data about hospitals. Overall, regressions were the most popular prediction methods (64.9%, 48/74), followed by machine learning (20.3%, 15/74) and deep learning (17.6%, 13/74). Regarding validation design, 35.1% (26/74) did not use a test set, whereas 47.3% (35/74) used a separate test set, and 17.6% (13/74) used cross-validation. The most used performance metrics were R2 (47.3%, 35/74), mean squared (or absolute) error (24.4%, 18/74), and the accuracy (14.9%, 11/74). Over the last decade, machine learning and deep learning methods became more popular (P=0.016), and test sets and cross-validation got more and more used (P=0.014). CONCLUSIONS: Methods to predict LOS are more and more elaborate and the assessment of their validity is increasingly rigorous. Reducing heterogeneity in how these methods are used and reported is key to transparency on their performance.

Improving patient self-reporting of antihypertensive adverse drug events in primary care: a stepped wedge cluster randomised trial.

BACKGROUND: About 25% of patients experience adverse drug events (ADE) in primary care, but few events are reported by the patients themselves. One solution to improve the detection and management of ADEs in primary care is for patients to report them to their general practitioner. The study aimed to assess the effect of a booklet designed to improve communication and interaction between patients treated with anti-hypertensive drugs and general practitioners on the reporting of ADEs. METHODS: A cluster randomized controlled cross-sectional stepped wedge open trial (five periods of 3 months) was conducted. A cluster was a group of general practitioners working in ambulatory offices in France. Adults consulting their general practitioner to initiate, modify, or renew an antihypertensive prescription were included. A booklet including information on cardiovascular risks, antihypertensive treatments, and ADE report forms was delivered by the general practitioner to the patient in the intervention group. The primary outcome was the reporting of at least one ADE by the patient to his general practitioner during the three-month period after enrolment. Two clusters were randomised by sequence for a total of 8 to receive the intervention. An intention-to-treat analysis was conducted. A logistic mixed model with random intercept was used. RESULTS: Sixty general practitioners included 1095 patients (median: 14 per general practitioner; range: 1-103). More patients reported at least one ADE to their general practitioner in the intervention condition compared to the control condition (aOR = 3.5, IC95 [1.2-10.1], p = 0.02). The modification and initiation of an antihypertensive treatment were also significantly associated with the reporting of ADEs (aOR = 4.4, CI95 [1.9-10.0], p <  0.001 and aOR = 11.0, CI95 [4.6-26.4], p <  0.001, respectively). The booklet delivery also improved patient satisfaction on general practitioner communication and high blood pressure management. CONCLUSION: A booklet can improve patient self-reporting of ADEs to their general practitioners. Future research should assess whether it can improve general practitioner management of ADEs and patient's health status. TRIAL REGISTRATION: Trial registry identifier NCT01610817 (2012/05/30).

The prediction of hospital length of stay using unstructured data.

OBJECTIVE: This study aimed to assess the performance improvement for machine learning-based hospital length of stay (LOS) predictions when clinical signs written in text are accounted for and compared to the traditional approach of solely considering structured information such as age, gender and major ICD diagnosis. METHODS: This study was an observational retrospective cohort study and analyzed patient stays admitted between 1 January to 24 September 2019. For each stay, a patient was admitted through the Emergency Department (ED) and stayed for more than two days in the subsequent service. LOS was predicted using two random forest models. The first included unstructured text extracted from electronic health records (EHRs). A word-embedding algorithm based on UMLS terminology with exact matching restricted to patient-centric affirmation sentences was used to assess the EHR data. The second model was primarily based on structured data in the form of diagnoses coded from the International Classification of Disease 10th Edition (ICD-10) and triage codes (CCMU/GEMSA classifications). Variables common to both models were: age, gender, zip/postal code, LOS in the ED, recent visit flag, assigned patient ward after the ED stay and short-term ED activity. Models were trained on 80% of data and performance was evaluated by accuracy on the remaining 20% test data. RESULTS: The model using unstructured data had a 75.0% accuracy compared to 74.1% for the model containing structured data. The two models produced a similar prediction in 86.6% of cases. In a secondary analysis restricted to intensive care patients, the accuracy of both models was also similar (76.3% vs 75.0%). CONCLUSIONS: LOS prediction using unstructured data had similar accuracy to using structured data and can be considered of use to accurately model LOS.

Hospital Length of Stay Reduction Over Time and Patient Readmission for Severe Adverse Events Following Surgery.

OBJECTIVE: The aim of the study was to investigate whether patients who undergo surgery in hospitals experiencing significant length of stay (LOS) reductions over time are exposed to a higher risk of severe adverse events in the postoperative period. SUMMARY BACKGROUND DATA: Surgical care innovation has encouraged hospitals to shorten LOS under financial pressures with uncertain impact on patient outcomes. METHODS: We selected all patients who underwent elective colectomy or urgent hip fracture repair in French hospitals between 2013 and 2016. For each procedure, hospitals were categorized into 3 groups according to variations in their median LOS as follows: major decrease, moderate decrease, and no decrease. These groups were matched using propensity scores based on patients' and hospitals' potential confounders. Potentially avoidable readmission for severe adverse events and death at 6 months were compared between groups using Cox regressions. RESULTS: We considered 98,713 patients in 540 hospitals for colectomy and 206,812 patients in 414 hospitals for hip fracture repair before matching. After colectomy, patient outcomes were not negatively impacted when hospitals reduced their LOS [hazard ratio (95% confidence interval): 0.93 (0.78-1.10)]. After hip fracture repair, patients in hospitals with major decreases in LOS had a higher risk of severe adverse events [1.22 (1.11-1.34)] and death [1.17 (1.04-1.32)]. CONCLUSIONS: Patients who underwent surgical procedures in hospitals experiencing major decreases in LOS were demonstrated worse postoperative outcomes after urgent hip fracture repair and not after elective colectomy. Development of care bundles to enhance recovery after emergency surgeries may allow better control of LOS reduction and patient outcomes.

Stress Management Training Improves Overall Performance during Critical Simulated Situations: A Prospective Randomized Controlled Trial.

BACKGROUND: High-fidelity simulation improves participant learning through immersive participation in a stressful situation. Stress management training might help participants to improve performance. The hypothesis of this work was that Tactics to Optimize the Potential, a stress management program, could improve resident performance during simulation. METHODS: Residents participating in high-fidelity simulation were randomized into two parallel arms (Tactics to Optimize the Potential or control) and actively participated in one scenario. Only residents from the Tactics to Optimize the Potential group received specific training a few weeks before simulation and a 5-min reactivation just before beginning the scenario. The primary endpoint was the overall performance during simulation measured as a composite score (from 0 to 100) combining a specific clinical score with two nontechnical scores (the Ottawa Global Rating Scale and the Team Emergency Assessment Measure scores) rated for each resident by four blinded independent investigators. Secondary endpoints included stress level, as assessed by the Visual Analogue Scale during simulation. RESULTS: Of the 134 residents randomized, 128 were included in the analysis. The overall performance (mean ± SD) was higher in the Tactics to Optimize the Potential group (59 ± 10) as compared with controls ([54 ± 10], difference, 5 [95% CI, 1 to 9]; P = 0.010; effect size, 0.50 [95% CI, 0.16 to 0.91]). After specific preparation, the median Visual Analogue Scale was 17% lower in the Tactics to Optimize the Potential group (52 [42 to 64]) than in the control group (63 [50 to 73]; difference, -10 [95% CI, -16 to -3]; P = 0.005; effect size, 0.44 [95% CI, 0.26 to 0.59]. CONCLUSIONS: Residents coping with simulated critical situations who have been trained with Tactics to Optimize the Potential showed better overall performance and a decrease in stress level during high-fidelity simulation. The benefits of this stress management training may be explored in actual clinical settings, where a 5-min Tactics to Optimize the Potential reactivation is feasible prior to delivering a specific intervention.

Elderly Patients with Metastatic Neuroendocrine Tumors Are Undertreated and Have Shorter Survival: The LyREMeNET Study.

INTRODUCTION: The incidence of neuroendocrine tumors (NETs) is rising, especially in elderly patients. The elderly cancer population presents considerable challenges, yet little is known about the characteristics, treatment patterns, and outcomes of metastatic NET (mNET) patients. METHODS: The Lyon Real-life Evidence in Metastatic NeuroEndocrine Tumors study (LyREMeNET, NCT03863106) included consecutive mNET patients, diagnosed between January 1990 and December 2017. The exclusion criteria were nonmetastatic NET, poorly differentiated neuroendocrine carcinoma, and mixed neuroendocrine-nonneuroendocrine neoplasms. We aimed to compare patients ≥70 years old to patients <70 years old. RESULTS: A total of 866 patients were included, 198 (23%) were ≥70 years old. There was no significant difference in characteristics except that elderly patients had synchronous metastasis more frequently. Elderly patients received significantly fewer treatments (median of 2.0 vs. 3.0 lines, respectively, p < 0.0001), were significantly less frequently treated by chemotherapy (32 vs. 54%), targeted therapy (16 vs. 30%), peptide receptor radionuclide therapy (5 vs. 16%), and they underwent significantly less frequently locoregional intervention. Median overall survival was significantly shorter in elderly patients (5.2 vs. 9.6 years). The most frequent cause of death was related to disease progression (71%). Multivariate analysis found that, after adjustment for tumor location, tumor grade, and number of metastatic sites, age remained significantly associated with overall survival (HR 1.66, 95% CI 1.26-2.18), indicating a poorer survival in patients ≥70 years old in comparison with younger patients (p = 0.0003). CONCLUSION: Patients ≥70 years old have a worse survival, die frequently from their disease, and are undertreated compared to younger patients.

Suicide Prevention Interventions for Older People in Nursing Homes and Long-Term Care Facilities: A Systematic Review.

BACKGROUND: The death rate due to suicide among older people is high, especially among men. Because many older people live in nursing homes or long-term care facilities in high-income countries, reviewing the impact of prevention strategies on the suicidal behavior of residents in these settings is of interest. METHODS: Following PRISMA guidelines, we performed a systematic review of the existing literature found in Pubmed, Scopus, Web of Science, PsycINFO, and Sociological Abstracts, focusing on interventions to prevent suicidal behavior or ideation in nursing home residents. The studies' quality was evaluated according to TIDieR and MMAT. RESULTS: Only 6 studies met the inclusion criteria. Four of them described various "gatekeeper" trainings for nursing home staff and 2 described interventions focused on residents. Only 1 study was randomized. Gatekeeper training studies were mostly before/after comparisons. No intervention demonstrated a direct effect on suicidal ideation or behaviors. One study showed that "life review" had a long-lasting effect on depression scores and another that gatekeeper training led to changes in the care of suicidal residents. CONCLUSIONS: Interventions to prevent suicidal ideation or behaviors in nursing homes are not rigorously evaluated, and no conclusion can be drawn on their effectiveness in preventing suicidal behaviors. We propose to better evaluate gatekeeper training for staff as well as peer support. Individual interventions targeting residents could be modified for broader implementation.

Serum Concentrations of Lidocaine During Bariatric Surgery.

Lidocaine has been shown to be clinically beneficial during bariatric surgery. However, information about lidocaine serum concentrations in this setting is scarce. This prospective clinical trial included 42 obese patients undergoing laparoscopic bariatric surgery. They received lidocaine based on adjusted body weight. Administration began with a 1.5 mg·kg bolus of intravenous lidocaine followed by a continuous infusion of 2 mg·kg·hour. After skin closure, administration was decreased to 1 mg·kg·hour until discharge from the recovery room. No serum concentrations of lidocaine were outside the usual accepted range (1.5-5 µg·mL).