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Background Peripheral nerve blocks are being used with increasing frequency for management of hip fracture–related pain. Despite converging evidence that nerve blocks may be beneficial, safety data are lacking. This study hypothesized that peripheral nerve block receipt would not be associated with adverse events potentially attributable to nerve blocks, as well as overall patient safety incidents while in hospital. Methods This was a preregistered, retrospective population-based cohort study using linked administrative data. This study identified all hip fracture admissions in people 50 yr of age or older and identified all nerve blocks (although we were unable to ascertain the specific anatomic location or type of block), potentially attributable adverse events (composite of seizures, fall-related injuries, cardiac arrest, nerve injury), and any patient safety events using validated codes. The study also estimated the unadjusted and adjusted association of nerve blocks with adverse events; adjusted absolute risk differences were also calculated. Results In total, 91,563 hip fracture patients from 2009 to 2017 were identified; 15,631 (17.1%) received a nerve block, and 5,321 (5.8%; 95% CI, 5.7 to 6.0%) patients experienced a potentially nerve block–attributable adverse event: 866 (5.5%) in patients with a block and 4,455 (5.9%) without a block. Before and after adjustment, nerve blocks were not associated with potentially attributable adverse events (adjusted odds ratio, 1.05; 95% CI, 0.97 to 1.15; and adjusted risk difference, 0.3%, 95% CI, –0.1 to 0.8). Conclusions The data suggest that nerve blocks in hip fracture patients are not associated with higher rates of potentially nerve block–attributable adverse events, although these findings may be influenced by limitations in routinely collected administrative data. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

In 2017, The Ottawa Hospital initiated a unique‐in‐Canada quality improvement initiative by opening a novel, multi‐specialist limb‐preservation clinic. We sought to describe the structure, processes, and initial outcomes of the clinic and evaluate whether it is achieving its mandate of providing high‐quality wound clinical care, education, and research. We conducted a descriptive prospective cohort study alongside a nested study of 162 clinic patients requiring serial assessments. There have been 1623 visits, mostly (72.2%) from outpatients. During 17.8% of visits, patients were evaluated by >1 specialist. Therapies provided most often included negative‐pressure wound therapy (32.7%), biological wound dressings (21.6%), and total contact casting (18.5%). Furthermore, 1.2% underwent toe/ray amputations or skin grafting in clinic and 22.8% were initiated on antimicrobials. Mixed‐effects models suggested that mean wound volumes for those requiring serial assessments decreased by 1.6 (95% confidence interval = −0.86 to −2.27) cm3 between visits. The clinic provided seven rotations to vascular surgery, infectious diseases, dermatology, and palliative care physicians; three nursing preceptorships; and two educational workshops. It also initiated provincial and national vascular health and wound care research initiatives. This study may be used to guide development of other limb‐preservation clinics and programmes. Findings support that our programme is achieving its mandate.

Study objective We aim to determine incidence and type of adverse events (adverse outcomes related to emergency care) among emergency department (ED) patients discharged with recent-onset atrial fibrillation, acute heart failure, and syncope. Methods This 5-year prospective cohort study included high-acuity adult patients discharged with the 3 sentinel diagnoses from 6 tertiary care Canadian EDs. We screened all ED visits for eligibility and performed telephone interviews 14 days postdischarge to identify flagged outcomes: death, hospital admission, return ED visit, health care provider visit, and new or worsening symptoms. We created case summaries describing index ED visit and flagged outcomes, and trained emergency physicians reviewed case summaries to identify adverse events. We reported adverse event incidence and rates with 95% confidence intervals and contributing factor themes. Results Among 4,741 subjects (mean age 70.2 years; 51.2% men), we observed 170 adverse events (3.6 per 100 patients; 95% confidence interval 3.1 to 4.2). Patients discharged with acute heart failure were most likely to experience adverse events (5.3%), followed by those with atrial fibrillation (2.0%) and syncope (0.8%). We noted variation in absolute adverse event rates across sites from 0.7 to 6.0 per 100 patients. The most common adverse event types were management issues, diagnostic issues, and unsafe disposition decisions. Frequent contributing factor themes included failure to recognize underlying causes and inappropriate management of dual diagnoses. Conclusion Among adverse events after ED discharge for patients with these 3 sentinel cardiovascular diagnoses, we identified quality improvement opportunities such as strengthening dual diagnosis detection and evidence-based clinical practice guideline adherence.

Seasonal respiratory viral infections are associated with exacerbations and morbidity among patients with COPD. The real-world clinical outcomes associated with seasonal viral infections are less well established among hospitalized patients.To estimate the association between seasonal respiratory viral infections, 30-day mortality, and intensive care unit (ICU) admission among hospitalized COPD patients.We conducted an analysis of a national prospective multicenter cohort of COPD patients hospitalized with acute respiratory illness during winter seasons (2011-2015) in Canada. Nasopharyngeal swabs were performed on all patients at the onset of hospital admission for diagnosis of viral infection. Primary outcomes were 30-day mortality and ICU admissions. Secondary outcomes included invasive/non-invasive ventilation use.Among 3931 hospitalized patients with COPD, 28.5% (1122/3931) were diagnosed with seasonal respiratory viral infection. Viral infection was associated with increased admission to ICU (OR 1.5, 95% CI 1.2-1.9) and need for mechanical ventilation (OR 1.9, 95% CI 1.4-2.5), but was not associated with mortality (OR 1.1, 95% CI 0.8-1.4). Patients with respiratory syncytial virus (RSV) were equally likely to require ICU admission (OR 1.09, 95% CI 0.67-1.78), and more likely to need non-invasive ventilation (OR 3.1; 95% CI 1.8-5.1) compared to patients with influenza.Our results suggest COPD patients requiring hospitalization for respiratory symptoms should routinely receive viral testing at admission, especially for RSV and influenza, to inform prognosis, clinical management, and infection control practices during winter seasons. Patients with COPD will be an important target population for newly developed RSV therapeutics.ClinicalTrials.gov ID: NCT01517191.

BACKGROUND: Many seriously ill hospitalized patients have cardiopulmonary resuscitation (CPR) as part of their care plan, but CPR is unlikely to achieve the goals of many seriously ill hospitalized patients. OBJECTIVE: To determine if a multicomponent decision support intervention changes documented orders for CPR in the medical record, compared to usual care. DESIGN: Open-label randomized controlled trial. PATIENTS: Patients on internal medicine and neurology wards at two tertiary care teaching hospitals who had a 1-year mortality greater than 10% as predicted with a validated model and whose care plan included CPR, if needed. INTERVENTION: Both the control and intervention groups received usual communication about CPR at the discretion of their care team. The intervention group participated in a values clarification exercise and watched a CPR video decision aid. MAIN MEASURE: The primary outcome was the proportion of patients who had a no-CPR order at 14 days after enrollment. KEY RESULTS: We recruited 200 patients between October 2017 and October 2018. Mean age was 77 years. There was no difference between the groups in no-CPR orders 14 days after enrollment (17/100 (17%) intervention vs 17/99 (17%) control, risk difference, − 0.2%) (95% confidence interval − 11 to 10%; p = 0.98). In addition, there were no differences between groups in decisional conflict summary score or satisfaction with decision-making. Patients in the intervention group had less conflict about understanding treatment options (decisional conflict knowledge subscale score mean (SD), 17.5 (26.5) intervention arm vs 40.4 (38.1) control; scale range 0–100 with lower scores reflecting less conflict). CONCLUSIONS: Among seriously ill hospitalized patients who had CPR as part of their care plan, this decision support intervention did not increase the likelihood of no-CPR orders compared to usual care. PRIMARY FUNDING SOURCE: Canadian Frailty Network, The Ottawa Hospital Academic Medical Organization. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11606-021-06605-y.

Acute heart failure patients often have an uncertain or delayed follow-up after discharge from the ED. Our goal was to introduce rapid-access specialty clinics to ensure acute heart failure patients were seen within 7 days, in an effort to reduce admissions and improve follow-up care. This prospective cohort study was conducted at two campuses of a large tertiary care hospital. We enrolled acute heart failure patients who presented to the ED with shortness of breath and were later discharged. Following a 12-month before period, we introduced rapid-access acute heart failure clinics staffed by cardiology and internal medicine. We allowed for a 3-month implementation period and then observed outcomes over the subsequent 12-month after period. The primary outcome was hospital admission within 30 days. Secondary outcomes included mortality and actual access to specialty care. Patients in the before (N = 355) and after periods (N = 374) were similar for age and most characteristics. Segmented autoregression analysis demonstrated there was a pre-existing trend to fewer admissions. Attendance at a specialty clinic increased from 17.8 to 42.1% (P

Objectives To examine long-term mortality, resource utilization, and healthcare costs in sepsis patients compared to hospitalized nonsepsis controls. Design Propensity-matched population-based cohort study using administrative data. Setting Ontario, Canada. Patients We identified a cohort of adults (≥ 18) admitted to hospitals in Ontario between April 1, 2012, and March 31, 2016, with follow-up to March 31, 2017. Sepsis patients were flagged using a validated International Classification of Diseases, 10th Revision-coded algorithm (Sepsis-2 definition), including cases with organ dysfunction (severe sepsis) and without (nonsevere). Remaining hospitalized patients were potential controls. Cases and controls were matched 1:1 on propensity score, age, sex, admission type, and admission date. Interventions None. Measurements and main results Differences in mortality, rehospitalization, hospital length of stay, and healthcare costs were estimated, adjusting for remaining confounders using Cox regression and generalized estimating equations. Of 270,669 sepsis cases, 196,922 (73%) were successfully matched: 64,204 had severe and 132,718 nonsevere sepsis (infection without organ dysfunction). Over follow-up (median 2.0 yr), severe sepsis patients had higher mortality rates than controls (hazard ratio, 1.66; 95% CI, 1.63-1.68). Both severe and nonsevere sepsis patients had higher rehospitalization rates than controls (hazard ratio, 1.53; 95% CI, 1.50-1.55 and hazard ratio, 1.41; 95% CI, 1.40-1.43, respectively). Incremental costs (Canadian dollar 2018) in sepsis cases versus controls at 1-year were: $29,238 (95% CI, $28,568-$29,913) for severe and $9,475 (95% CI, $9,150-$9,727) for nonsevere sepsis. Conclusions Severe sepsis was associated with substantially higher long-term risk of death, rehospitalization, and healthcare costs, highlighting the need for effective postdischarge care for sepsis survivors.

Background Frailty is associated with early postoperative outcomes. How frailty influences long-term postoperative recovery is poorly described. Our objective was to evaluate the association of frailty with postoperative disability trajectories in the year after surgery. Methods Prespecified 1-yr follow-up of a prospective multicentre cohort study. Patients ≥65 yr were assessed for frailty before major elective noncardiac surgery (Clinical Frailty Scale [CFS] and Fried Phenotype [FP]). The primary outcome was patient-reported disability score (using the WHO Disability Assessment Schedule 2.0) at baseline, 30, 90, and 365 days after surgery. Repeated measures linear regression estimated the association of preoperative frailty with changes in disability scores over time, adjusted for procedure. Group-based trajectory modelling was used to identify subgroup trajectories of people with frailty. Results One-year follow-up was complete for 687/702 (97.9%) participants. Frailty was associated with a significant difference in disability trajectory (P Conclusions Decision-making and care planning should integrate the possible trade-offs between early adverse outcomes with longer-term benefit when frailty is present in older surgical patients.

Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 disease. There are concerns regarding limited testing capacity and the exclusion of cases from unproven screening criteria. Knowing COVID-19 risks can inform testing. This study derived and assessed a model to predict risk of SARS-CoV-2 in community-based people. Methods All people presenting to a community-based COVID-19 screening center answered questions regarding symptoms, possible exposure, travel, and occupation. These data were anonymously linked to SARS-CoV-2 testing results. Logistic regression was used to derive a model to predict SARS-CoV-2 infection. Bootstrap sampling evaluated the model. Results A total of 9172 consecutive people were studied. Overall infection rate was 6.2% but this varied during the study period. SARS-CoV-2 infection likelihood was primarily influenced by contact with a COVID-19 case, fever symptoms, and recent case detection rates. Internal validation found that the SARS-CoV-2 Risk Prediction Score (SCRiPS) performed well with good discrimination (c-statistic 0.736, 95%CI 0.715–0.757) and very good calibration (integrated calibration index 0.0083, 95%CI 0.0048–0.0131). Focusing testing on people whose expected SARS-CoV-2 risk equaled or exceeded the recent case detection rate would increase the number of identified SARS-CoV-2 cases by 63.1% (95%CI 54.5–72.3). Conclusion The SCRiPS model accurately estimates the risk of SARS-CoV-2 infection in community-based people undergoing testing. Using SCRiPS can importantly increase SARS-CoV-2 infection identification when testing capacity is limited. Electronic supplementary material The online version of this article (10.1007/s11606-020-06307-x) contains supplementary material, which is available to authorized users.

Patients want personalized information before surgery; most do not receive personalized risk estimates. Inadequate information contributes to poor experience and medicolegal complaints. We hypothesized that exposure to the Personalized Risk Evaluation and Decision Making in Preoperative Clinical Assessment (PREDICT) app, a personalized risk communication tool, would improve patient knowledge and satisfaction after anesthesiology consultations compared with standard care. We conducted a prospective clinical study (before-after design) and used patient-reported data to calculate personalized risks of morbidity, mortality, and expected length of stay using a locally calibrated National Surgical Quality Improvement Program risk calculator embedded in the PREDICT app. In the standard care (before) phase, the application’s materials and output were not available to participants; in the PREDICT app (after) phase, personalized risks were communicated. Our primary outcome was knowledge score after the anesthesiology consultation. Secondary outcomes included patient satisfaction, anxiety, feasibility, and acceptability. We included 183 participants (90 before; 93 after). Compared with standard care phase, the PREDICT app phase had higher post-consultation: knowledge of risks (14.3% higher; 95% confidence interval [CI], 6.5 to 22.0; P < 0.001) and satisfaction (0.8 points; 95% CI, 0.1 to 1.4; P = 0.03). Anxiety was unchanged (− 1.9%; 95% CI, − 4.2 to 0.5; P = 0.13). Acceptability was high for patients and anesthesiologists. Exposure to a patient-facing, personalized risk communication app improved knowledge of personalized risk and increased satisfaction for adults before elective inpatient surgery. www.clinicaltrials.gov (NCT03422133); registered 5 February 2018.

Many healthcare organizations have adopted the quadruple aim to create system-level improvements for delivering enhanced experience and outcomes to patients, healthier populations, reduced per-capita costs, and better provider experiences. With a maturing health technology sector, virtual care is gradually being adopted in Canada and proving to be a viable tactic for achieving the quadruple aim. Despite increased acceptance of virtual innovations and their related benefits to patients and providers, implementation of virtual care can be challenging in a Canadian healthcare system. The Ottawa Hospital developed an innovation strategy to guide the adoption and maturity of virtual care as a means of supporting the pursuit of the quadruple aim and achievement of the organization’s mission and vision. A case example presenting the strategy and recommendations for health leaders and providers considering implementation of virtual care is discussed.

Background: There is increasing evidence that premature newborns and infants with low birth weight can benefit substantially from an exclusive human milk-based diet (EHMD), consisting of human milk...

BackgroundFrailty is a robust predictor of poor outcomes among patients with chronic obstructive pulmonary disease yet is not measured in routine practice. We determined barriers and facilitators to measuring frailty in a hospital setting, designed and implemented a frailty-focused education intervention, and measured accuracy of frailty screening before and after education.MethodsWe conducted a pilot cross-sectional mixed-methods study on an inpatient respiratory ward over 6 months. We recruited registered nurses (RNs) with experience using the Clinical Frailty Scale (CFS). RNs evaluated 10 clinical vignettes and assigned a frailty score using the CFS. A structured frailty-focused education intervention was delivered to small groups. RNs reassigned frailty scores to vignettes 1 week after education. Outcomes included barriers and facilitators to assessing frailty in hospital, and percent agreement of CFS scores between RNs and a gold standard (determined by geriatricians) before and after education.ResultsAmong 26 RNs, the median (IQR) duration of experience using the CFS was 1.5 (1–4) months. Barriers to assessing frailty included the lack of clinical directives to measure frailty and large acute workloads. Having collateral history from family members was the strongest perceived facilitator for frailty assessment. The median (IQR) percent agreement with the gold-standard frailty score across all cases was 55.8% (47.2%–60.6%) prior to the educational intervention, and 57.2% (44.1%–70.2%) afterwards. The largest increase in agreement occurred in the ‘mildly frail’ category, 65.4%–81% agreement.ConclusionsBarriers to assessing frailty in the hospital setting are external to the measurement tool itself. Accuracy of frailty assessment among acute care RNs was low, and frailty-focused rater training may improve accuracy. Subsequent work should focus on health system approaches to empower health providers to assess frailty, and on testing the effectiveness of frailty-focused education in large real-world settings.

BackgroundThe implementation and continuous improvement of patient safety learning systems (PSLS) is a principal strategy for mitigating preventable harm to patients. Although substantial efforts have sought to improve these systems, there is a need to more comprehensively understand critical success factors. This study aims to summarise the barriers and facilitators perceived by hospital staff and physicians to influence the reporting, analysis, learning and feedback within PSLS in hospitals.MethodsWe performed a systematic review and meta-synthesis by searching MEDLINE (Ovid), EMBASE (Ovid), CINAHL, Scopus and Web of Science. We included English-language manuscripts of qualitative studies evaluating effectiveness of the PSLS and excluded studies evaluating specific individual adverse events, such as systems for tracking only medication side effects, for example. We followed the Joanna Briggs Institute methodology for qualitative systematic reviews.ResultsWe extracted data from 22 studies, after screening 2475 for inclusion/exclusion criteria. The included studies focused on reporting aspects of the PSLS, however, there were important barriers and facilitators across the analysis, learning and feedback phases. We identified the following barriers for effective use of PSLS: inadequate organisational support with shortage of resources, lack of training, weak safety culture, lack of accountability, defective policies, blame and a punitive environment, complex system, lack of experience and lack of feedback. We identified the following enabling factors: continuous training, a balance between accountability and responsibility, leaders as role models, anonymous reporting, user-friendly systems, well-structured analysis teams, tangible improvement.ConclusionMultiple barriers and facilitators to uptake of PSLS exist. These factors should be considered by decision makers seeking to enhance the impact of PSLS.Ethics and disseminationNo formal ethical approval or consent were required as no primary data were collected.

Frailty is a state of vulnerability as a result of decreased reserves. Prehabilitation may increase reserve and improve postoperative outcomes. Our objective was to determine if home-based prehabilitation improves postoperative functional recovery in older adults with frailty having cancer surgery.This double blind randomised trial enrolled people ≥60 yr having elective cancer surgery and ≥3 weeks from enrolment to surgery as eligible. Participation in a remotely supported, home-based exercise prehabilitation program plus nutritional guidance was compared with standard care plus written advice on age-appropriate activity and nutrition. The primary outcome was 6-min walk test (6MWT) distance at the first postoperative clinic visit. Secondary outcomes included physical performance, quality of life, disability, length of stay, non-home discharge, and 30-day readmission.Of 543 patients assessed, 254 were eligible and 204 (80%) were randomised (102 per arm); 182 (94 intervention and 88 control) had surgery and were analysed. Mean age was 74 yr and 57% were female. Mean duration of participation was 5 weeks, mean adherence was 61% (range 0%-100%). We found no significant difference in 6MWT at follow-up (+14 m, 95% confidence interval -26-55 m, P=0.486), or for secondary outcomes. Analyses using a prespecified adherence definition of ≥80% supported improvements in 6MWT distance, complication count, and disability.A home-based prehabilitation program did not significantly improve postoperative recovery or other outcomes in older adults with frailty having cancer surgery. Program adherence may be a key mediator of prehabilitation efficacy.NCT02934230.

ImportanceFrailty is associated with severe morbidity and mortality among people with chronic obstructive pulmonary disease (COPD). Interventions such as pulmonary rehabilitation can treat and reverse frailty, yet frailty is not routinely measured in pulmonary clinical practice. It is unclear how population-based administrative data tools to screen for frailty compare with standard bedside assessments in this population.ObjectiveTo determine the agreement between the Hospital Frailty Risk Score (HFRS) and the Clinical Frailty Scale (CFS) among hospitalized individuals with COPD and to determine the sensitivity and specificity of the HFRS (vs CFS) to detect frailty.Design, Setting, and ParticipantsA cross-sectional study was conducted among hospitalized patients with COPD exacerbation. The study was conducted in the respiratory ward of a single tertiary care academic hospital (The Ottawa Hospital, Ottawa, Ontario, Canada). Participants included consenting adult inpatients who were admitted with a diagnosis of acute COPD exacerbation from December 2016 to June 2019 and who used a clinical care pathway for COPD. There were no specific exclusion criteria. Data analysis was performed in March 2022.ExposureDegree of frailty measured by the CFS.Main Outcomes and MeasuresThe HFRS was calculated using hospital administrative data. Primary outcomes were the sensitivity and specificity of the HFRS to detect frail and nonfrail individuals according to CFS assessments of frailty, and the secondary outcome was the optimal probability threshold of the HFRS to discriminate frail and nonfrail individuals.ResultsAmong 99 patients with COPD exacerbation (mean [SD] age, 70.6 [9.5] years; 56 women [57%]), 14 (14%) were not frail, 33 (33%) were vulnerable, 18 (18%) were mildly frail, and 34 (34%) were moderately to severely frail by the CFS. The HFRS (vs CFS) had a sensitivity of 27% and specificity of 93% to detect frail vs nonfrail individuals. The optimal probability threshold for the HFRS was 1.4 points or higher. The corresponding sensitivity to detect frailty was 69%, and the specificity was 57%.Conclusions and RelevanceIn this cross-sectional study, using the population-based HFRS to screen for frailty yielded poor detection of frailty among hospitalized patients with COPD compared with the bedside CFS. These findings suggest that use of the HFRS in this population may result in important missed opportunities to identify and provide early intervention for frailty, such as pulmonary rehabilitation.

ObjectivesNormal saline (NS) and Ringer’s lactate (RL) are the most common crystalloids used for fluid therapy. Despite evidence of possible harm associated with NS (eg, hyperchloremic metabolic acidosis, impaired kidney function and death), few large multi-centre randomised trials have evaluated the effect of these fluids on clinically important outcomes. We conducted a pilot trial to explore the feasibility of a large trial powered for clinically important outcomes.DesignFLUID was a pragmatic pilot cluster randomised cross-over trial.SettingFour hospitals in the province of Ontario, CanadaParticipantsAll hospitalised adult and paediatric patients with an incident admission to the hospital over the course of each study period.InterventionsA hospital wide policy/strategy which stocked either NS or RL throughout the hospital for 12 weeks before crossing over to the alternate fluid for the subsequent 12 weeks.Primary and secondary outcome measuresThe primary feasibility outcome was study fluid protocol adherence. Secondary feasibility outcomes included time to Research Ethics Board (REB) approval and trial initiation. Primary (composite of death or re-admission to hospital in first 90 days of index hospitalisation) and secondary clinical outcomes were analysed descriptively.ResultsAmong 24 905 included patients, mean age 59.1 (SD 20.5); 13 977 (56.1%) were female and 21 150 (85.0%) had medical or surgical admitting diagnoses. Overall, 96 821 L were administered in the NS arm, and 78 348 L in the RL arm. Study fluid adherence to NS and RL was 93.7% (site range: 91.6%–98.0%) and 79.8% (site range: 72.5%–83.9%), respectively. Time to REB approval ranged from 2 to 48 days and readiness for trial initiation from 51 to 331 days. 5544 (22.3%) patients died or required hospital re-admission in the first 90 days.ConclusionsThe future large trial is feasible. Anticipating and addressing logistical challenges during the planning stages will be imperative.Trial registration numberNCT02721485.

BACKGROUND Guidelines recommend routine preoperative frailty assessment for older people. However, the degree to which frailty instruments improve predictive accuracy when added to traditional risk factors is poorly described. Our objective was to measure the accuracy gained in predicting outcomes important to older patients when adding the Clinical Frailty Scale (CFS), Fried Phenotype (FP), or Frailty Index (FI) to traditional risk factors. METHODS This was an analysis of a multicenter prospective cohort of elective noncardiac surgery patients ≥65 years of age. Each frailty instrument was prospectively collected. The added predictive performance of each frailty instrument beyond the baseline model (age, sex, American Society of Anesthesiologists' score, procedural risk) was estimated using likelihood ratio test, discrimination, calibration, explained variance, and reclassification. Outcomes analyzed included death or new disability, prolonged length of stay (LoS, >75th percentile), and adverse discharge (death or non-home discharge). RESULTS We included 645 participants (mean age, 74 [standard deviation, 6]); 72 (11.2%) participants died or experienced a new disability, 164 (25.4%) had prolonged LoS, and 60 (9.2%) had adverse discharge. Compared to the baseline model predicting death or new disability (area under the curve [AUC], 0.67; R, 0.08, good calibration), prolonged LoS (AUC, 0.73; R, 0.18, good calibration), and adverse discharge (AUC, 0.78; R, 0.16, poor calibration), the CFS improved fit per the likelihood ratio test (P < .02 for death or new disability

ObjectiveAdministrative data systems are used to identify hospital-based patient safety events; few studies evaluate their accuracy. We assessed the accuracy of a new set of patient safety indicators (PSIs; designed to identify in hospital complications).Study designProspectively defined analysis of registry data (1 April 2010–29 February 2016) in a Canadian hospital network. Assignment of complications was by two methods independently. The National Surgical Quality Improvement Programme (NSQIP) database was the clinical reference standard (primary outcome=any in-hospital NSQIP complication); PSI clusters were assigned using International Classification of Disease (ICD-10) codes in the discharge abstract. Our primary analysis assessed the accuracy of any PSI condition compared with any complication in the NSQIP; secondary analysis evaluated accuracy of complication-specific PSIs.PatientsAll inpatient surgical cases captured in NSQIP data.AnalysisWe assessed the accuracy of PSIs (with NSQIP as reference standard) using positive and negative predictive values (PPV/NPV), as well as positive and negative likelihood ratios (±LR).ResultsWe identified 12 898 linked episodes of care. Complications were identified by PSIs and NSQIP in 2415 (18.7%) and 2885 (22.4%) episodes, respectively. The presence of any PSI code had a PPV of 0.55 (95% CI 0.53 to 0.57) and NPV of 0.93 (95% CI 0.92 to 0.93); +LR 6.41 (95% CI 6.01 to 6.84) and −LR 0.40 (95% CI 0.37 to 0.42). Subgroup analyses (by surgery type and urgency) showed similar performance. Complication-specific PSIs had high NPVs (95% CI 0.92 to 0.99), but low to moderate PPVs (0.13–0.61).ConclusionValidation of the ICD-10 PSI system suggests applicability as a first screening step, integrated with data from other sources, to produce an adverse event detection pathway that informs learning healthcare systems. However, accuracy was insufficient to directly identify or rule out individual-level complications.

BackgroundWe have designed a prospective adverse event (AE) surveillance method. We performed this study to evaluate this method’s performance in several hospitals simultaneously.ObjectivesTo compare AE rates obtained by prospective AE surveillance in different hospitals and to evaluate measurement factors explaining observed variation.MethodsWe conducted a multicentre prospective observational study. Prospective AE surveillance was implemented for 8 weeks on the general medicine wards of five hospitals. To determine if population factors may have influenced results, we performed mixed-effects logistic regression. To determine if surveillance factors may have influenced results, we reassigned observers to different hospitals midway through surveillance period and reallocated a random sample of events to different expert review teams.ResultsDuring 3560 patient days of observation of 1159 patient encounters, we identified 356 AEs (AE risk per encounter=22%). AE risk varied between hospitals ranging from 9.9% of encounters in Hospital D to 35.8% of encounters in Hospital A. AE types and severity were similar between hospitals—the most common types were related to clinical procedures (45%), hospital-acquired infections (21%) and medications (19%). Adjusting for age and comorbid status, we observed an association between hospital and AE risk. We observed variation in observer behaviour and moderate agreement between clinical reviewers, which could have influenced the observed rate difference.ConclusionThis study demonstrated that it is possible to implement prospective surveillance in different settings. Such surveillance appears to be better suited to evaluating hospital safety concerns within rather than between hospitals as we could not definitively rule out whether the observed variation in AE risk was due to population or surveillance factors.

Objectives Polypharmacy is common, costly, and harmful for hospitalized older adults. Scalable strategies to reduce the burden of potentially inappropriate medications (PIMs) are needed. We sought to leverage medication reconciliation in hospitalized older adults by pairing with MedSafer, an electronic decision support tool for deprescribing. Design This was a nonrandomized controlled before-and-after study. Setting The study took place on four internal medicine clinical teaching units. Participants Subjects were aged 65 years and older, had an expected prognosis of 3 or more months, and were taking five or more usual home medications. Intervention In the baseline phase, patients received usual care that was medication reconciliation. Patients in the intervention arm also had a "deprescribing opportunity report" generated by MedSafer and provided to their in-hospital treating team. Measurements The primary outcome was ascertained at the time of hospital discharge and was the proportion of patients who had one or more PIMs deprescribed. Results A total of 1066 patients were enrolled, and deprescribing opportunities were present for 873 (82%; 418 during the control and 455 during the intervention phases, respectively). The proportion of patients with one or more PIMs deprescribed at discharge increased from 46.9% in the control period to 54.7% in the intervention period with an adjusted absolute risk difference of 8.3% (2.9%-13.9%). Not all classes of drugs in the intervention arm were associated with an increase in deprescribing, and new PIM starts were equally common in both arms of the study. Conclusion Using an electronic decision support tool for deprescribing, we increased the proportion of patients with one or more PIMs deprescribed at hospital discharge as compared with usual care. Although this type of intervention may help address medication overload in hospitalized patients, it also underscores the importance of powering future trials for a reduction in adverse drug events. Trial registration NCT02918058. J Am Geriatr Soc 67:1843-1850, 2019.

We describe The Ottawa Hospital’s (TOH) journey to create a Just Culture. We will describe the concept of a Just Culture, why TOH initiated this transformation, how TOH went about creating the Just Culture, and some of its early impacts. Following two events that called into question our hospital's safety culture, the hospital leadership adopted a deliberate and methodical organization-wide approach to change. These efforts included generating leadership commitment, incorporating the efforts within its corporate strategy, obtaining stakeholder engagement, developing and delivering an education program, and last but not least, efforts to improve safety systems. TOH has attempted to develop an increased focus on safety—for staff, visitors, and patients. The Ottawa Hospital has had demonstrable success throughout this journey as a result of a disciplined effort to create a Just Culture. This work will require ongoing efforts to ensure the culture shift is sustained.

Summary Background Healthcare-acquired Clostridium difficile infection (HA-CDI) is a common infection and a financial burden on the healthcare system. Aim To estimate the hospital-based financial costs of HA-CDI by comparing time-fixed statistical models that attribute cost to the entire hospital stay to time-varying statistical models that adjust for the time between admission, diagnosis of HA-CDI, and discharge and that only attribute HA-CDI costs post diagnosis. Methods A retrospective cohort study was conducted (April 2008 to March 2011) using clinical and administrative costing data of inpatients (≥15 years) who were admitted to The Ottawa Hospital with stays >72 h. Two time-fixed analyses, ordinary least square regression and generalized linear regression, were contrasted with two time-dependent approaches using Kaplan–Meier survival curve. Findings A total of 49,888 admissions were included and 366 (0.73%) patients developed HA-CDI. Estimated total costs (Canadian dollars) from time-fixed models were as high as $74,928 per patient compared to $28,089 using a time-varying model, and these were 1.47-fold higher compared to a patient without HA-CDI (incremental cost $8,997 per patient). The overall annual institutional cost at The Ottawa Hospital associated with HA-CDI was as high as $10.07 million using time-fixed models and $1.62 million using time-varying models. Conclusion When calculating costs associated with HA-CDI, accounting for the time between admission, diagnosis, and discharge can substantially reduce the estimated institutional costs associated with HA-CDI.

Purpose There is wide variation in the utilization of Intensive Care Unit (ICU) beds for treatment and monitoring of adult patients with Diabetic Ketoacidosis (DKA). We sought to compare the outcomes and hospital costs of adult DKA patients admitted to ICUs as compared to those admitted to step-down units. Materials and methods We included consecutive adult patients from two hospitals with a diagnosis of DKA. Patients were either admitted to the ICU, or a step-down unit, which has a nurse-to-patient ratio of 2:1, but does not have capability for mechanical ventilation or administration of vasoactive agents. The primary outcome was in-hospital mortality. Results We included 872 patients in the analysis. 71 (8.1%) were admitted to ICU, while 801 (91.9%) were admitted to a step-down unit. We found no difference in in-hospital mortality between patients admitted to the ICU and those admitted to the step-down unit (adjusted odds ratio [OR]: 1.14, 95% confidence interval [CI]: 0.87–2.64). Mean total hospital costs were significantly higher for patients admitted to the ICU ($20,428 vs. $6484, P Conclusions Adult DKA patients admitted to a step-down unit had comparable in-hospital mortality and lower hospital costs as compared to those admitted to the ICU.

Background Since primary data collection can be time-consuming and expensive, surgical site infections (SSIs) could ideally be monitored using routinely collected administrative data. We derived and internally validated efficient algorithms to identify SSIs within 30 days after surgery with health administrative data, using Machine Learning algorithms. Methods All patients enrolled in the National Surgical Quality Improvement Program from the Ottawa Hospital were linked to administrative datasets in Ontario, Canada. Machine Learning approaches, including a Random Forests algorithm and the high-performance logistic regression, were used to derive parsimonious models to predict SSI status. Finally, a risk score methodology was used to transform the final models into the risk score system. The SSI risk models were validated in the validation datasets. Results Of 14,351 patients, 795 (5.5%) had an SSI. First, separate predictive models were built for three distinct administrative datasets. The final model, including hospitalization diagnostic, physician diagnostic and procedure codes, demonstrated excellent discrimination (C statistics, 0.91, 95% CI, 0.90–0.92) and calibration (Hosmer-Lemeshow χ2 statistics, 4.531, p = 0.402). Conclusion We demonstrated that health administrative data can be effectively used to identify SSIs. Machine learning algorithms have shown a high degree of accuracy in predicting postoperative SSIs and can integrate and utilize a large amount of administrative data. External validation of this model is required before it can be routinely used to identify SSIs.

Background After nonelective (i.e., semiurgent, urgent and emergent) surgeries, patients discharged from hospitals are at risk of readmissions, emergency department visits or death. During the coronavirus disease 2019 (COVID-19) pandemic, we are undertaking the Post Discharge after Surgery Virtual Care with Remote Automated Monitoring Technology (PVC-RAM) trial to determine if virtual care with remote automated monitoring (RAM) compared with standard care will increase the number of days adult patients remain alive at home after being discharged following nonelective surgery. Methods We are conducting a randomized controlled trial in which 900 adults who are being discharged after nonelective surgery from 8 Canadian hospitals are randomly assigned to receive virtual care with RAM or standard care. Outcome adjudicators are masked to group allocations. Patients in the experimental group learn how to use the study's tablet computer and RAM technology, which will measure their vital signs. For 30 days, patients take daily biophysical measurements and complete a recovery survey. Patients interact with nurses via the cellular modem-enabled tablet, who escalate care to preassigned and available physicians if RAM measurements exceed predetermined thresholds, patients report symptoms, a medication error is identified or the nurses have concerns they cannot resolve. The primary outcome is number of days alive at home during the 30 days after randomization. Interpretation This trial will inform management of patients after discharge following surgery in the COVID-19 pandemic and offer insights for management of patients who undergo nonelective surgery in a nonpandemic setting. Knowledge dissemination will be supported through an online multimedia resource centre, policy briefs, presentations, peer-reviewed journal publications and media engagement. Trial registration ClinicalTrials.gov, no. NCT04344665.

Background: Since primary data collection can be time-consuming and expensive, surgical site infections (SSIs) could ideally be monitored using routinely collected administrative data. We derived and internally validated efficient algorithms to identify SSIs within 30 days after surgery with health administrative data, using Machine Learning algorithms. All patients enrolled in the National Surgical Quality Improvement Program from the Ottawa Hospital were linked to administrative datasets in Ontario, Canada. Machine Learning approaches, including a Random Forests algorithm and the high-performance logistic regression, were used to derive parsimonious models to predict SSI status. Finally, a risk score methodology was used to transform the final models into the risk score system. The SSI risk models were validated in the validation datasets.Results: Of 14,351 patients, 795 (5.5%) had an SSI. First, separate predictive models were built for three distinct administrative datasets. The final model, including hospitalization diagnostic, physician diagnostic and procedure codes, demonstrated excellent discrimination (C statistics, 0.91, 95% CI, 0.90-0.92) and calibration (Hosmer-Lemeshow χ2 statistics, 4.531, p=0.402). Conclusion: We demonstrated that health administrative data can be effectively used to identify SSIs. Machine learning algorithms have shown a high degree of accuracy in predicting postoperative SSIs and can integrate and utilize a large amount of administrative data. External validation of this model is required before it can be routinely used to identify SSIs.