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To determine factors leading to interhospital care fragmentation (ICF); evaluate how ICF affects rehospitalization costs, length of stays (LOS), and delayed discharge; and analyze ICF disparity among equity-seeking groups. We used a 13-year retrospective cohort of older adults (65+) in Ontario, Canada. Utilizing multivariable logistic regression, we identified characteristics associated with ICF and determined its association with outcomes. Discharge to facilities except home and homecare and travel distance were the strongest risk factors for ICF. Patients were less likely to experience ICF if they were older, frail, or had multiple comorbidities. ICF was strongly associated with an increase in the daily costs of readmission. Moreover, the risks of a prolonged LOS after ICF and delayed discharge were higher among returning surgical patients. The rural residency was a source of health inequality. ICF exacerbates health disparities and worsens patient outcomes. Our study identified several risk factors associated with ICF, some of which are controllable, paving the way for interventions to mitigate this issue. To promote health equity and reduce adverse outcomes, policymakers should focus on policies for reducing care discontinuity, particularly addressing the controllable risk factors.

Conventional gabaminergic sedatives such as benzodiazepines and propofol are commonly used in mechanically ventilated patients in the intensive care unit (ICU). Dexmedetomidine is an alternative sedative that may achieve lighter sedation, reduce delirium, and provide analgesia. Our objective was to perform a comprehensive systematic review summarizing the large body of evidence, determining if dexmedetomidine reduces delirium compared to conventional sedatives. We searched MEDLINE, EMBASE, CENTRAL, ClinicalTrials.gov and the WHO ICTRP from inception to October 2021. Independent pairs of reviewers identified randomized clinical trials comparing dexmedetomidine to other sedatives for mechanically ventilated adults in the ICU. We conducted meta-analyses using random-effects models. The results were reported as relative risks (RRs) for binary outcomes and mean differences (MDs) for continuous outcomes, with corresponding 95% confidence intervals (CIs). In total, 77 randomized trials (n = 11,997) were included. Compared to other sedatives, dexmedetomidine reduced the risk of delirium (RR 0.67, 95% CI 0.55 to 0.81; moderate certainty), the duration of mechanical ventilation (MD − 1.8 h, 95% CI  – 2.89 to  – 0.71; low certainty), and ICU length of stay (MD  – 0.32 days, 95% CI  – 0.42 to  – 0.22; low certainty). Dexmedetomidine use increased the risk of bradycardia (RR 2.39, 95% CI 1.82 to 3.13; moderate certainty) and hypotension (RR 1.32, 95% CI 1.07 to 1.63; low certainty). In mechanically ventilated adults, the use of dexmedetomidine compared to other sedatives, resulted in a lower risk of delirium, and a modest reduction in duration of mechanical ventilation and ICU stay, but increased the risks of bradycardia and hypotension.

The increasing prevalence of type 2 diabetes (T2D) is a significant health concern worldwide. Effective and personalized treatment strategies are essential for improving patient outcomes and reducing healthcare costs. Machine learning (ML) has the potential to create clinical decision support systems (CDSS) that assist clinicians in making prediction-informed treatment decisions. This study aims to develop a novel predictive-prescriptive analytics framework that leverages ML to enhance medication prescriptions for T2D patients. The framework is designed as a data-driven CDSS to determine the best medication strategies based on individual patient profiles, including demographics, comorbidities, and medications. Utilizing a comprehensive dataset of electronic health records from 17,773 patients across various U.S. Veterans Administration Medical Centers collected over 12 years, the study employs the Bayesian Network (BN) as the ML model of choice. The BN’s unique dual capability serves both predictive and prescriptive functions. Several BN learning algorithms are applied to map the relationships among patient features and decision variables for predicting the outcome. The prescriptive stage includes three strategies, i.e., forward, backward, and guideline-based, to identify optimal treatment recommendations. Next, the complex treatment pathways identified through the prescriptive stage were illustrated using rule-based and decision-tree presentations to improve interpretability for actionable insights and clinical usability. Finally, our empirical analysis examines the alignment between recommended treatment strategies and actual physician prescriptions. ML exhibited strong predictive performance with a precision of 0.789, a recall of 0.879, and an F1-score of 0.831. The recommended treatment strategies aligned with physician prescriptions in simpler treatment scenarios. However, the alignment decreased as the complexity of medication prescription increased, highlighting the challenges of achieving physician compliance with optimal strategies in complex scenarios. This underscores the greater need for CDSS, particularly in situations involving complex combination therapy. This study presents a novel ML-based CDSS framework for personalized T2D treatment. Leveraging ML, the framework offers a promising approach to optimizing medication prescriptions and improving patient outcomes.

PurposeStress ulcer prophylaxis (SUP) is commonly prescribed in the intensive care unit. However, data from systematic reviews and conventional meta-analyses are limited by imprecision and restricted to direct comparisons. We conducted a network meta-analysis of randomized clinical trials (RCTs) to examine the safety and efficacy of drugs available for SUP in critically ill patients.MethodsWe searched MEDLINE, EMBASE, and the Cochrane Library Central Register of Controlled Trials through April 2017 for randomized controlled trials that examined the efficacy and safety of proton pump inhibitors (PPIs), histamine-2 receptor antagonists (H2RAs), and sucralfate for SUP in critically ill patients. No date or language restrictions were applied. Data on study characteristics, methods, outcomes, and risk of bias were abstracted by two reviewers.ResultsOf 96 potentially eligible studies, we included 57 trials enrolling 7293 patients. The results showed that PPIs are probably more effective for preventing clinically important gastrointestinal bleeding (CIB) than H2RAs [odds ratio (OR) 0.38; 95% confidence interval (95% CI) 0.20, 0.73], sucralfate (OR 0.30; 95% CI 0.13, 0.69), and placebo (OR 0.24; 95% CI 0.10, 0.60) (all moderate quality evidence). There were no convincing differences among H2RA, sucralfate, and placebo. PPIs probably increase the risk of developing pneumonia compared with H2RAs (OR 1.27; 95% CI 0.96, 1.68), sucralfate (OR 1.65; 95% CI 1.20, 2.27), and placebo (OR 1.52; 95% CI 0.95, 2.42) (all moderate quality). Mortality is probably similar across interventions (moderate quality). Estimates of baseline risks of bleeding varied significantly across studies, and only one study reported on Clostridium difficile infection. Definitions of pneumonia varied considerably. Most studies on sucralfate predate pneumonia prevention strategies.ConclusionsOur results provide moderate quality evidence that PPIs are the most effective agents in preventing CIB, but they may increase the risk of pneumonia. The balance of benefits and harms leaves the routine use of SUP open to question.

Background The relative efficacy and safety of proton pump inhibitors (PPIs) compared to histamine-2-receptor antagonists (H2RAs) should guide their use in reducing bleeding risk in the critically ill. Methods We searched the Cochrane library, MEDLINE, EMBASE, ACPJC, clinical trials registries, and conference proceedings through November 2015 without language or publication date restrictions. Only randomized controlled trials (RCTs) of PPIs vs H2RAs for stress ulcer prophylaxis in critically ill adults for clinically important bleeding, overt gastrointestinal (GI) bleeding, nosocomial pneumonia, mortality, ICU length of stay and Clostridium difficile infection were included. We used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to assess our confidence in the evidence for each outcome. Results In 19 trials enrolling 2117 patients, PPIs were more effective than H2RAs in reducing the risk of clinically important GI bleeding (RR 0.39; 95 % CI 0.21, 0.71; P  = 0.002; I 2  = 0 %, moderate confidence) and overt GI bleeding (RR 0.48; 95 % CI 0.34, 0.66; P  < 0.0001; I 2  = 3 %, moderate confidence). PPI use did not significantly affect risk of pneumonia (RR 1.12; 95 % CI 0.86, 1.46; P  = 0.39; I 2  = 2 %, low confidence), mortality (RR 1.05; 95 % CI 0.87, 1.27; P  = 0.61; I 2  = 0 %, moderate confidence), or ICU length of stay (mean difference (MD), –0.38 days; 95 % CI –1.49, 0.74; P  = 0.51; I 2  = 30 %, low confidence). No RCT reported Clostridium difficile infection. Conclusions PPIs were superior to H2RAs in preventing clinically important and overt GI bleeding, without significantly increasing the risk of pneumonia or mortality. Their impact on Clostridium difficile infection is yet to be determined.

Sepsis is a major public and global health concern. Every hour of delay in detecting sepsis significantly increases the risk of death, highlighting the importance of accurately predicting sepsis in a timely manner. A growing body of literature has examined developing new or improving the existing machine learning (ML) approaches for timely and accurate predictions of sepsis. This study contributes to this literature by providing clear insights regarding the role of the recency and adequacy of historical information in predicting sepsis using ML. To this end, we implemented a deep learning model using a bidirectional long short-term memory (BiLSTM) algorithm and compared it with six other ML algorithms based on numerous combinations of the prediction horizons (to capture information recency) and observation windows (to capture information adequacy) using different measures of predictive performance. Our results indicated that the BiLSTM algorithm outperforms all other ML algorithms and provides a great separability of the predicted risk of sepsis among septic versus non-septic patients. Moreover, decreasing the prediction horizon (in favor of information recency) always boosts the predictive performance; however, the impact of expanding the observation window (in favor of information adequacy) depends on the prediction horizon and the purpose of prediction. More specifically, when the prediction is responsive to the positive label (i.e., Sepsis), increasing historical data improves the predictive performance when the prediction horizon is short-moderate.

IntroductionThe cuff leak test (CLT) is hypothesised to help optimise extubation by assessing for laryngeal oedema which, if unrecognised and untreated, could lead to post-extubation stridor, post-extubation airway obstruction, and reintubation. However, the diagnostic accuracy of the CLT to detect post-extubation stridor (and hence potentially airway obstruction) remains uncertain. Given the equipoise that exists surrounding the CLT, we are conducting a pilot randomised clinical trial (RCT) examining the CLT as part of the pathway to extubation. Herein, we report the protocol for the Cuff Leak Test and Airway Obstruction in Mechanically Ventilated ICU Patients (COSMIC): a Pilot Feasibility Randomized Clinical trial (RCT).Methods and analysisThis is a multicentre, international, parallel-group, pragmatic, pilot RCT. We will enrol 100 mechanically ventilated patients in the intensive care unit (ICU) who are deemed ready for extubation and have at least one risk factor for laryngeal oedema. In the intervention arm, respiratory therapists will perform a qualitative CLT before extubation. If a patient passes the CLT (suggesting no laryngeal oedema), extubation will be performed in keeping with standard care. If the patient fails the CLT (suggesting laryngeal oedema), extubation will be delayed allowing for administration of dexamethasone, consideration of diuresis, and the CLT will be repeated in 12–24 hours. In the control arm, patients will be extubated without completing a CLT, without steroid administration, and without delay. Randomization will be by a 1:1 allocation, stratified by centre. The primary feasibility outcomes will include recruitment and protocol adherence. Secondary outcomes will include post-extubation stridor, reintubation within 72 hours, emergency surgical airway within 72 hours, and ICU and hospital mortality within 30 days.Ethics and disseminationThis trial has been approved by Clinical Trials Ontario, Hamilton Integrated Research Ethics Board, State of Kuwait Ministry of Health, University of Texas Health Committee for the Protection of Human Subjects and Brant Community Health Systems Research Ethics Committee. The trial has received a No Objection Letter from Health Canada. Trial results will be disseminated via publication in peer-reviewed journals.Trial registration numberNCT05456542.

Anxiety is a serious personal health condition and represents a substantial burden to overall quality of life. Additionally anxiety disorders represent a significant cost to the health care system as well as employers through benefits coverage and days missed due to incapacity. This study sought to explore the effectiveness of naturopathic care on anxiety symptoms using a randomized trial. Employees with moderate to severe anxiety of longer than 6 weeks duration were randomized based on age and gender to receive naturopathic care (NC) (n = 41) or standardized psychotherapy intervention (PT) (n = 40) over a period of 12 weeks. Blinding of investigators and participants during randomization and allocation was maintained. Participants in the NC group received dietary counseling, deep breathing relaxation techniques, a standard multi-vitamin, and the herbal medicine, ashwagandha (Withania somnifera) (300 mg b.i.d. standardized to 1.5% with anolides, prepared from root). The PT intervention received psychotherapy, and matched deep breathing relaxation techniques, and placebo. The primary outcome measure was the Beck Anxiety Inventory (BAI) and secondary outcome measures included the Short Form 36 (SF-36), Fatigue Symptom Inventory (FSI), and Measure Yourself Medical Outcomes Profile (MY-MOP) to measure anxiety, mental health, and quality of life respectively. Participants were blinded to the placebo-controlled intervention. Seventy-five participants (93%) were followed for 8 or more weeks on the trial. Final BAI scores decreased by 56.5% (p<0.0001) in the NC group and 30.5% (p<0.0001) in the PT group. BAI group scores were significantly decreased in the NC group compared to PT group (p = 0.003). Significant differences between groups were also observed in mental health, concentration, fatigue, social functioning, vitality, and overall quality of life with the NC group exhibiting greater clinical benefit. No serious adverse reactions were observed in either group. Many patients seek alternatives and/or complementary care to conventional anxiety treatments. To date, no study has evaluated the potential of a naturopathic treatment protocol to effectively treat anxiety. Knowledge of the efficacy, safety or risk of natural health products, and naturopathic treatments is important for physicians and the public in order to make informed decisions. Both NC and PT led to significant improvements in patients' anxiety. Group comparison demonstrated a significant decrease in anxiety levels in the NC group over the PT group. Significant improvements in secondary quality of life measures were also observed in the NC group as compared to PT. The whole system of naturopathic care for anxiety needs to be investigated further including a closer examination of the individual components within the context of their additive effect. Controlled-Trials.com ISRCTN78958974.

Background Scaling-up and sustaining healthcare interventions can be challenging. Our objective was to describe how the 3 Wishes Project (3WP), a personalized end-of-life intervention, was scaled-up and sustained in an intensive care unit (ICU). Methods In a longitudinal mixed-methods study from January 12,013 - December 31, 2018, dying patients and families were invited to participate if the probability of patient death was > 95% or after a decision to withdraw life support. A research team member or bedside clinician learned more about each of the patients and their family, then elicited and implemented at least 3 personalized wishes for patients and/or family members. We used a qualitative descriptive approach to analyze interviews and focus groups conducted with 25 clinicians who cared for the enrolled patients. We used descriptive statistics to summarize patient, wish, and clinician characteristics, and analyzed outcome data in quarters using Statistical Process Control charts. The primary outcome was enrollment of terminally ill patients and respective families; the secondary outcome was the number of wishes per patient; tertiary outcomes included wish features and stakeholder involvement. Results Both qualitative and quantitative analyses suggested a three-phase approach to the scale-up of this intervention during which 369 dying patients were enrolled, having 2039 terminal wishes implemented. From a research project to clinical program to an approach to practice, we documented a three-fold increase in enrolment with a five-fold increase in total wishes implemented, without a change in cost. Beginning as a study, the protocol provided structure; starting gradually enabled frontline staff to experience and recognize the value of acts of compassion for patients, families, and clinicians. The transition to a clinical program was marked by handover from the research staff to bedside staff, whereby project catalysts mentored project champions to create staff partnerships, and family engagement became more intentional. The final transition involved empowering staff to integrate the program as an approach to care, expanding it within and beyond the organization. Conclusions The 3WP is an end-of-life intervention which was implemented as a study, scaled-up into a clinical program, and sustained by becoming integrated into practice as an approach to care.

Background Seniors with recurrent hospitalizations who are taking multiple medications including high-risk medications are at particular risk for serious adverse medication events. We will assess whether an expert Clinical Pharmacology and Toxicology (CPT) medication management intervention during hospitalization with follow-up post-discharge and communication with circle of care is feasible and can decrease drug therapy problems amongst this group. Methods The design is a pragmatic pilot randomized trial with 1:1 patient-level concealed randomization with blinded outcome assessment and data analysis. Participants will be adults 65 years and older admitted to internal medicine services for more than 2 days, who have had at least one other hospitalization in the prior year, taking five or more chronic medications including at least one high-risk medication. The CPT intervention identifies medication targets; completes consult, including priorities for improving prescribing negotiated with the patient; starts the care plan; ensures a detailed discharge medication reconciliation and circle-of-care communication; and sees the patient at least twice after hospital discharge via virtual visits to consolidate the care plan in the community. Control group receives usual care. Primary outcomes are feasibility — recruitment, retention, costs, and clinical — number of drug therapy problems improved, with secondary outcomes examining coordination of transitions in care, quality of life, and healthcare utilization and costs. Follow-up is to 3-month posthospital discharge. Discussion If results support feasibility of ramp-up and promising clinical outcomes, a follow-up definitive trial will be organized using a developing national platform and medication appropriateness network. Since the intervention allows a very scarce medical specialty expertise to be offered via virtual care, there is potential to improve the safety, outcomes, and cost of care widely. Trial registration number ClinicalTrials.gov identifier: NCT04077281.