Nursing resource team capacity planning using forecasting and optimization methods: A case study.

AIM: To estimate the cost-minimizing size and skill mix of a nursing resource team (NRT). BACKGROUND: Nurse absences can be filled by an NRT at lower hourly cost than staffing agencies or nurses working overtime, but an NRT must be appropriately sized to minimize total cost. METHODS: Using all registered nurse (RN) absences at an academic teaching hospital from 1 October 2014 to 31 March 2018, we developed a generalized additive model (GAM) to forecast the weekly frequency of each of ten types of absence over 52 weeks. We used the forecasts in an optimization model to determine the cost-minimizing NRT composition. RESULTS: The median weekly frequencies for the ten absence types ranged between 12 and 65.5. The root mean squared errors of the GAMs ranged between 4.55 and 9.07 on test data. The NRT dimensioned by the optimization model yields an estimated annual cost reduction of $277,683 (Canadian dollars) (7%). CONCLUSIONS: The frequency of RN absences in a hospital can be forecasted with high accuracy, and the use of forecasting and optimization to dimension an NRT can substantially reduce the cost of filling RN absences. IMPLICATIONS FOR NURSING MANAGEMENT: This methodology can be adapted by any hospital to optimize nurse staffing.

Characterizing the impact of snowfall on patient attendance at an urban emergency department in Toronto, Canada.

OBJECTIVES: We sought to determine whether addition of a snowfall variable improves emergency department (ED) patient volume forecasting. Our secondary objective was to characterize the magnitude of effect of snowfall on ED volume. METHODS: We used daily historical patient volume data and local snowfall records from April 1st, 2011 to March 31st, 2018 (2542 days) to fit a series of four generalized linear models: a baseline model which included calendar variables and three different snowfall models with an indicator variable for either any snowfall (>0 cm), moderate snowfall (≥1 cm), or large snowfall (≥5 cm). To evaluate model fit, we examined the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Incident rate ratios were calculated to determine the effect of snowfall in each model. RESULTS: All three snowfall models demonstrated improved model fit compared to the model without snowfall. The best fitting model included a binary variable for snowfall (<1 cm vs. ≥1 cm). This model showed a statistically significant decrease in daily ED volume of 2.65% (95% CI: 1.23%-4.00%) on snowfall days. DISCUSSION: The addition of a snowfall variable results in improved model performance in short-term ED volume forecasting. Snowfall is associated with a modest, but statistically significant reduction in ED volume.

Efficacy of intraoperative cell salvage in decreasing perioperative blood transfusion rates in first-time cardiac surgery patients: a retrospective study.

BACKGROUND: Evidence regarding the safety and efficacy of intraoperative cell salvage (ICS) in transfusion reduction during cardiac surgery remains conflicting. We sought to evaluate the impact of routine ICS on outcomes following cardiac surgery. METHODS: We conducted a retrospective analysis of patients who underwent nonemergent, first-time cardiac surgery 18 months before and 18 months after the implementation of routine ICS. Perioperative transfusion rates, postoperative bleeding, clinical and hematological outcomes, and overall cost were examined. We used multivariable logistic regression modelling to determine the risk-adjusted effect of ICS on likelihood of perioperative transfusion. RESULTS: A total of 389 patients formed the final study population (186 undergoing ICS and 203 controls). Patients undergoing ICS had significantly lower perioperative transfusion rates of packed red blood cells (pRBCs; 33.9% v. 45.3% p = 0.021), coagulation products (16.7% v. 32.5% p < 0.001) and any blood product (38.2% v. 52.7%, p = 0.004). Patients receiving ICS had decreased mediastinal drainage at 12 h (mean 320 [range 230-550] mL v. mean 400 [range 260-690] mL, p = 0.011) and increased postoperative hemoglobin (mean 104.7 ± 13.2 g/L v. 95.0 ± 11.9 g/L, p < 0.001). Following adjustment for other baseline and intraoperative covariates, ICS emerged as an independent predictor of lower perioperative transfusion rates of pRBCs (odds ratio [OR] 0.52, 95% confidence interval [CI] 0.31-0.87), coagulation products (OR 0.41, 95% CI 0.24-0.71) and any blood product (OR 0.47, 95% CI 0.29-0.77). Additionally, ICS was associated with a cost benefit of $116 per patient. CONCLUSION: Intraoperative cell salvage could represent a clinically cost-effective way of reducing transfusion rates in patients undergoing cardiac surgery. Further research on systematic ICS is required before recommending it for routine use.

CONTEXTE: Les résultats d'études portant sur l'innocuité et l'efficacité de l'autotransfusion peropératoire (ATPO) comme mesure de réduction du besoin de transfusion durant une chirurgie cardiaque sont contradictoires. Nous avons cherché à évaluer l'incidence du recours systématique à l'ATPO sur les issues de chirurgies cardiaques. MÉTHODES: Nous avons mené une analyse rétrospective portant sur des patients ayant subi une première chirurgie cardiaque non urgente 18 mois avant et 18 mois après l'introduction de l'ATPO systématique. Les taux de transfusion périopératoire et d'hémorragie postopératoire, les résultats cliniques et hématologiques et le coût total ont été analysés. Nous avons utilisé un modèle de régression logistique multivariée pour déterminer l'incidence ajustée en fonction du risque du recours à l'ATPO sur la probabilité qu'une transfusion périopératoire soit nécessaire. RÉSULTATS: L'échantillon à l'étude était composé de 389 patients (186 dans le groupe ATPO et 203 dans le groupe témoin). Par rapport au groupe témoin, les patients ayant reçu une ATPO ont eu besoin significativement moins souvent d'une transfusion de concentrés de globules rouges (33,9 % c. 45,3 %; p = 0,021), de produits coagulants (16,7 % c. 32,5 %; p < 0,001) et de produits sanguins, tous types confondus (38,2 % c. 52,7 %; p = 0,004). Chez les patients ayant reçu une ATPO, on a constaté un volume de drainage médiastinal après 12 h plus faible (moyenne : 320 mL [étendue de 230-550] c. 400 mL [étendue de 260-690]; p = 0,011) et une hémoglobine postopératoire plus élevée (moyenne : 104,7 ± 13,2 g/L c. 95,0 ± 11,9 g/L; p < 0,001). Après des ajustements pour tenir compte d'autres covariables des mesures de base et peropératoires, nous avons conclu que le recours à l'ATPO était un facteur prédicteur indépendant de taux de transfusion périopératoire plus faibles de concentré de globules rouges (rapport de cotes [RC] : 0,52; intervalle de confiance [IC] à 95 % : 0,31-0,87), de produits coagulants (RC : 0,41; IC à 95 % : 0,24-0,71) et de produits sanguins, tous types confondus (RC : 0,47; IC à 95 % : 0,29-0,77). De plus, l'ATPO a été associée à des économies de 116 $ par patient. CONCLUSION: L'autotransfusion peropératoire pourrait constituer un moyen cliniquement efficace en fonction des coûts de réduire les taux de transfusion des patients subissant une chirurgie cardiaque. D'autres recherches sur le recours systématique à l'ATPO devront être menées avant qu'on puisse recommander son utilisation de routine.

Mammographic microcalcifications and breast cancer tumorigenesis: a radiologic-pathologic analysis.

BACKGROUND: Microcalcifications (MCs) are tiny deposits of calcium in breast soft tissue. Approximately 30% of early invasive breast cancers have fine, granular MCs detectable on mammography; however, their significance in breast tumorigenesis is controversial. This study had two objectives: (1) to find associations between mammographic MCs and tumor pathology, and (2) to compare the diagnostic value of mammograms and breast biopsies in identifying malignant MCs. METHODS: A retrospective chart review was performed for 937 women treated for breast cancer during 2000-2012 at St. Michael's Hospital. Demographic information (age and menopausal status), tumor pathology (size, histology, grade, nodal status and lymphovascular invasion), hormonal status (ER and PR), HER-2 over-expression and presence of MCs were collected. Chi-square tests were performed for categorical variables and t-tests were performed for continuous variables. All p-values less than 0.05 were considered statistically significant. RESULTS: A total of 937 patient charts were included. About 38.3% of the patients presented with mammographic MCs on routine mammographic screening. Patients were more likely to have MCs if they were HER-2 positive (52.9%; p < 0.001). There was a significant association between MCs and peri-menopausal status with a mean age of 50 (64%; p = 0.012). Patients with invasive ductal carcinomas (40.9%; p = 0.001) were more likely to present with MCs than were patients with other tumor histologies. Patients with a heterogeneous breast density (p = 0.031) and multifocal breast disease (p = 0.044) were more likely to have MCs on mammograms. There was a positive correlation between MCs and tumor grade (p = 0.057), with grade III tumors presenting with the most MCs (41.3%). A total of 52.2% of MCs were missed on mammograms which were visible on pathology (p < 0.001). CONCLUSION: This is the largest study suggesting the appearance of MCs on mammograms is strongly associated with HER-2 over-expression, invasive ductal carcinomas, peri-menopausal status, heterogeneous breast density and multifocal disease.

Incidence of febrile neutropenia in early stage breast cancer patients receiving adjuvant FEC-D treatment.

PURPOSE: The purpose of this study is to determine the incidence of febrile neutropenia (FN) among women receiving FEC-D (flurouracil 500 mg/m(2), epirubicin 100 mg/m(2), and cyclophosphamide 500 mg/m(2) every 3 weeks for three cycles followed by docetaxel 100 mg/m(2) every 3 weeks for three cycles) chemotherapy for early stage breast cancer (ESBC) and the impact of primary granulocyte colony-stimulating factor (G-CSF) prophylaxis in a non-clinical trial setting. PATIENTS AND METHODS: A retrospective chart review of women referred for ESBC to The Moncton Hospital between 2005 and 2010 evaluated patient and disease characteristics, adjuvant chemotherapy receipt, G-CSF usage, FN incidence, hospital admission rates, and length of stay. Association of variables with FN was examined, and exploratory multivariable logistic regression modeling examined the impact of baseline variables on risk of FN. RESULTS: Of 520 patients enrolled in the database, 251 (48.3 %) received adjuvant chemotherapy for ESBC. Most (66.9 %) received FEC-D. Overall, 55 (21.9 %) patients developed FN. Forty-four (26.2 %) patients on FEC-D developed FN. Forty of 129 (31.0 %) FEC-D patients who did not receive primary G-CSF prophylaxis developed FN, versus 4 of 39 (10.3 %) receiving G-CSF. Receipt of FEC-D or TC (docetaxel 75 mg/m(2) and cyclophosphamide 600 mg/m(2) every 3 weeks for four or six cycles) was associated with odds ratios of 6.5 or 6.77, respectively, for the development of FN. Receipt of trastuzumab with chemotherapy was associated with an odds ratio of 3.48 for developing FN versus no trastuzumab. Primary G-CSF prophylaxis led to a 63 % reduction in the odds ratio of developing FN. CONCLUSIONS: Incidence of FN with FEC-D treatment is considerably higher in clinical practice than reported in phase III trials. Consistent with ASCO guidelines, prophylactic G-CSF should be considered for all ESBC patients receiving adjuvant FEC-D.

Emergency department use: is frequent use associated with a lack of primary care provider?

OBJECTIVE: To determine if having a primary care provider is an important factor in frequency of emergency department (ED) use. DESIGN: Analysis of a central computerized health network database. SETTING: Three EDs in southern New Brunswick. PARTICIPANTS: All ED visits during 1 calendar year to an urban regional hospital (URH), an urban urgent care centre (UCC), and a rural community hospital (RCH) were captured. MAIN OUTCOME MEASURES: Patients with and without listed primary care providers were compared in terms of number of visits to the ED. A logistic regression analysis was used to determine factors predictive of frequent attendance. RESULTS: In total, 48 505, 41 004, and 27 900 visits were made to the URH, UCC, and RCH, respectively, in 2009. The proportion of patients with listed primary care providers was 36.6% for the URH, 37.1% for the UCC, and 89.4% for the RCH. Among ED patients at all sites, frequent attenders (4 or more visits to an ED in 1 year) were significantly more likely (59.6% vs 45.1%, P < .001) to have listed primary care providers. Other factors that predicted frequent use included attendance at a rural ED, female sex, and older age. CONCLUSION: This study characterizes attendance rates for 3 EDs in southern New Brunswick. Our findings highlight interesting differences between urban and rural ED populations, and suggest that frequent use of the ED might not be related to lack of a listed primary care provider.

Developing and Validating a Prediction Model For Death or Critical Illness in Hospitalized Adults, an Opportunity for Human-Computer Collaboration.

Hospital early warning systems that use machine learning (ML) to predict clinical deterioration are increasingly being used to aid clinical decision-making. However, it is not known how ML predictions complement physician and nurse judgment. Our objective was to train and validate a ML model to predict patient deterioration and compare model predictions with real-world physician and nurse predictions. DESIGN: Retrospective and prospective cohort study. SETTING: Academic tertiary care hospital. PATIENTS: Adult general internal medicine hospitalizations. MEASUREMENTS AND MAIN RESULTS: We developed and validated a neural network model to predict in-hospital death and ICU admission in 23,528 hospitalizations between April 2011 and April 2019. We then compared model predictions with 3,374 prospectively collected predictions from nurses, residents, and attending physicians about their own patients in 960 hospitalizations between April 30, and August 28, 2019. ML model predictions achieved clinician-level accuracy for predicting ICU admission or death (ML median F1 score 0.32 [interquartile range (IQR) 0.30-0.34], AUC 0.77 [IQ 0.76-0.78]; clinicians median F1-score 0.33 [IQR 0.30-0.35], AUC 0.64 [IQR 0.63-0.66]). ML predictions were more accurate than clinicians for ICU admission. Of all ICU admissions and deaths, 36% occurred in hospitalizations where the model and clinicians disagreed. Combining human and model predictions detected 49% of clinical deterioration events, improving sensitivity by 16% compared with clinicians alone and 24% compared with the model alone while maintaining a positive predictive value of 33%, thus keeping false alarms at a clinically acceptable level. CONCLUSIONS: ML models can complement clinician judgment to predict clinical deterioration in hospital. These findings demonstrate important opportunities for human-computer collaboration to improve prognostication and personalized medicine in hospital.

From compute to care: Lessons learned from deploying an early warning system into clinical practice.

Background: Deploying safe and effective machine learning models is essential to realize the promise of artificial intelligence for improved healthcare. Yet, there remains a large gap between the number of high-performing ML models trained on healthcare data and the actual deployment of these models. Here, we describe the deployment of CHARTwatch, an artificial intelligence-based early warning system designed to predict patient risk of clinical deterioration. Methods: We describe the end-to-end infrastructure that was developed to deploy CHARTwatch and outline the process from data extraction to communicating patient risk scores in real-time to physicians and nurses. We then describe the various challenges that were faced in deployment, including technical issues (e.g., unstable database connections), process-related challenges (e.g., changes in how a critical lab is measured), and challenges related to deploying a clinical system in the middle of a pandemic. We report various measures to quantify the success of the deployment: model performance, adherence to workflows, and infrastructure uptime/downtime. Ultimately, success is driven by end-user adoption and impact on relevant clinical outcomes. We assess our deployment process by evaluating how closely we followed existing guidance for good machine learning practice (GMLP) and identify gaps that are not addressed in this guidance. Results: The model demonstrated strong and consistent performance in real-time in the first 19 months after deployment (AUC 0.76) as in the silent deployment heldout test data (AUC 0.79). The infrastructure remained online for >99% of time in the first year of deployment. Our deployment adhered to all 10 aspects of GMLP guiding principles. Several steps were crucial for deployment but are not mentioned or are missing details in the GMLP principles, including the need for a silent testing period, the creation of robust downtime protocols, and the importance of end-user engagement. Evaluation for impacts on clinical outcomes and adherence to clinical protocols is underway. Conclusion: We deployed an artificial intelligence-based early warning system to predict clinical deterioration in hospital. Careful attention to data infrastructure, identifying problems in a silent testing period, close monitoring during deployment, and strong engagement with end-users were critical for successful deployment.

Harnessing the full potential of hospital-based data to support surgical quality improvement.

Surgical departments commonly rely on third-party quality improvement registries. As electronic health data become increasingly integrated and accessible within an institution, alternatives to these platforms arise. We present the conceptualization and implementation of an in-house quality improvement platform that provides real-time reports, is less onerous on clinicians and is tailored to an institution's priorities of care.

Cohort profile: St. Michael's Hospital Tuberculosis Database (SMH-TB), a retrospective cohort of electronic health record data and variables extracted using natural language processing.

BACKGROUND: Tuberculosis (TB) is a major cause of death worldwide. TB research draws heavily on clinical cohorts which can be generated using electronic health records (EHR), but granular information extracted from unstructured EHR data is limited. The St. Michael's Hospital TB database (SMH-TB) was established to address gaps in EHR-derived TB clinical cohorts and provide researchers and clinicians with detailed, granular data related to TB management and treatment. METHODS: We collected and validated multiple layers of EHR data from the TB outpatient clinic at St. Michael's Hospital, Toronto, Ontario, Canada to generate the SMH-TB database. SMH-TB contains structured data directly from the EHR, and variables generated using natural language processing (NLP) by extracting relevant information from free-text within clinic, radiology, and other notes. NLP performance was assessed using recall, precision and F1 score averaged across variable labels. We present characteristics of the cohort population using binomial proportions and 95% confidence intervals (CI), with and without adjusting for NLP misclassification errors. RESULTS: SMH-TB currently contains retrospective patient data spanning 2011 to 2018, for a total of 3298 patients (N = 3237 with at least 1 associated dictation). Performance of TB diagnosis and medication NLP rulesets surpasses 93% in recall, precision and F1 metrics, indicating good generalizability. We estimated 20% (95% CI: 18.4-21.2%) were diagnosed with active TB and 46% (95% CI: 43.8-47.2%) were diagnosed with latent TB. After adjusting for potential misclassification, the proportion of patients diagnosed with active and latent TB was 18% (95% CI: 16.8-19.7%) and 40% (95% CI: 37.8-41.6%) respectively. CONCLUSION: SMH-TB is a unique database that includes a breadth of structured data derived from structured and unstructured EHR data by using NLP rulesets. The data are available for a variety of research applications, such as clinical epidemiology, quality improvement and mathematical modeling studies.

Extracting Clinical Features From Dictated Ambulatory Consult Notes Using a Commercially Available Natural Language Processing Tool: Pilot, Retrospective, Cross-Sectional Validation Study.

BACKGROUND: The increasing adoption of electronic health records (EHRs) in clinical practice holds the promise of improving care and advancing research by serving as a rich source of data, but most EHRs allow clinicians to enter data in a text format without much structure. Natural language processing (NLP) may reduce reliance on manual abstraction of these text data by extracting clinical features directly from unstructured clinical digital text data and converting them into structured data. OBJECTIVE: This study aimed to assess the performance of a commercially available NLP tool for extracting clinical features from free-text consult notes. METHODS: We conducted a pilot, retrospective, cross-sectional study of the accuracy of NLP from dictated consult notes from our tuberculosis clinic with manual chart abstraction as the reference standard. Consult notes for 130 patients were extracted and processed using NLP. We extracted 15 clinical features from these consult notes and grouped them a priori into categories of simple, moderate, and complex for analysis. RESULTS: For the primary outcome of overall accuracy, NLP performed best for features classified as simple, achieving an overall accuracy of 96% (95% CI 94.3-97.6). Performance was slightly lower for features of moderate clinical and linguistic complexity at 93% (95% CI 91.1-94.4), and lowest for complex features at 91% (95% CI 87.3-93.1). CONCLUSIONS: The findings of this study support the use of NLP for extracting clinical features from dictated consult notes in the setting of a tuberculosis clinic. Further research is needed to fully establish the validity of NLP for this and other purposes.

Physician recruitment and retention in New Brunswick: a medical student perspective.

BACKGROUND: Physician recruitment and retention is a priority for many Canadian provinces. Each province is unique in terms of recruitment strategies and packages offered; however, little is known about how medical students evaluate these programs. The purpose of the current study was to determine which factors matter most to New Brunswick (NB) medical students when considering their location of future practice. METHOD: A survey of NB medical students was conducted. Descriptive statistics were produced and a linear regression model was developed to study factors predictive of a student's expressed willingness to practice in NB. RESULTS: 158 medical students completed the online survey, which is a response rate of 55%. Job availability and spouse's ability to work in the province were ranked as the top factors in deciding where to practice. In the final regression model, factors predictive of an expressed desire to practice in NB include being female, living in NB prior to medical school, attending medical school at Université de Sherbrooke, participation in the NB Preceptorship program, and a desire to practice family medicine. CONCLUSIONS: This study provides insight into what medical students consider when deciding where to practice. This research may be used to inform physician recruitment efforts and guide future research into medical education and policy.

Application of the Beers Criteria to Alternate Level of Care Patients in Hospital Inpatient Units.

BACKGROUND: The Beers criteria were developed to help in identifying potentially inappropriate medications (PIMs) for elderly patients. These medications are often associated with adverse events and limited effectiveness in older adults. Patients awaiting an alternate level of care (ALC patients) are those who no longer require acute care hospital services and are waiting for placement elsewhere. They are often elderly, have complex medication regimens, and are at high risk of adverse events. At the time of this study no studies had applied the Beers criteria to ALC patients in Canadian hospitals. OBJECTIVES: To determine the proportion of ALC patients receiving PIMs and the proportion experiencing selected PIM-related adverse events. METHODS: A retrospective chart review of ALC patients 65 years of age or older was performed to identify PIMs and the occurrence of selected adverse events (specifically central nervous system [CNS] events, falls, bradycardia, hypoglycemia, seizures, insomnia, gastrointestinal bleeding, and urinary tract infections). A logistic regression model with a random intercept for each patient was constructed to estimate odds ratios and probabilities of adverse events. RESULTS: Fifty-two ALC patients were included in the study. Of these, 48 (92%) were taking a PIM. Of the 922 adverse events evaluated, 407 (44.1%) were associated with a regularly scheduled PIM. Among patients who were taking regularly scheduled PIMs, there was a significantly increased probability of an adverse CNS event and of a fall (p < 0.001 for both). The most common PIM medication classes were first-generation antihistamines (24 [46%] of the 52 patients), antipsychotics (21 patients [40%]), short-acting benzodiazepines (15 patients [29%]), and nonbenzodiazepine hypnotics (14 patients [27%]). CONCLUSIONS: A high proportion of ALC patients were taking PIMs and experienced an adverse event that may have been related to these drugs. These findings suggest that the ALC population might benefit from regular medication review and monitoring to prevent or detect adverse events.

CONTEXTE: Les critères de Beers ont été élaborés afin d'aider à détecter l'utilisation de médicaments potentiellement inappropriés (MPI) auprès des patients âgés. L'on associe souvent les MPI à des événements indésirables, et leur efficacité chez les personnes âgées est limitée. Les patients en attente d'un autre niveau de soins (patients ANS) sont ceux qui ne nécessitent plus de soins de courte durée de l'hôpital et qui attendent d'être déplacés vers un autre établissement. Il s'agit souvent de personnes âgées ayant une panoplie complexe de traitements médicamenteux et présentant un risque élevé de subir des événements indésirables. Au moment de la présente recherche, aucune étude n'avait appliqué les critères de Beers aux patients ANS des hôpitaux canadiens. OBJECTIFS: Déterminer quelles sont les proportions de patients ANS qui reçoivent des MPI et qui subissent certains événements indésirables choisis liés à ces médicaments. MÉTHODES: Une analyse rétrospective des dossiers médicaux de patients ANS âgés de 65 ans et plus a été réalisée dans le but de relever les MPI ainsi que les cas de certains événements indésirables choisis (particulièrement les événements liés au système nerveux central, les chutes, la bradycardie, l'hypoglycémie, les convulsions, l'insomnie, les hémorragies gastro-intestinales et les infections urinaires). On a mis au point un modèle de régression logistique avec ordonnée à l'origine aléatoire pour chaque patient afin d'estimer les risques relatifs approchés ainsi que les probabilités d'événements indésirables. RÉSULTATS: Au total, 52 patients ANS ont été admis à l'étude. De ceuxci, 48 (92 %) prenaient un MPI. Des 922 événements indésirables analysés, 407 (44,1 %) ont été associés à un MPI administré régulièrement. Parmi les patients prenant des MPI à une fréquence régulière, la probabilité de subir une chute ou un événement indésirable lié au système nerveux central était grandement accrue (p < 0,001 pour chacun). Les MPI les plus fréquents étaient : les antihistaminiques de première génération (24 [46 %] des 52 patients), les antipsychotiques (21 patients [40 %]), les benzodiazépines à action brève (15 patients [29 %]) et les hypnotiques non-benzodiazépines (14 patients [27 %]). CONCLUSIONS: Un grand nombre de patients ANS prenaient des MPI et avaient subi un événement indésirable qui pouvait avoir été en lien avec ces médicaments. Ces résultats laissent croire que les patients ANS pourraient tirer avantage d'évaluations fréquentes de la pharmacothérapie et de surveillance afin de prévenir les événements indésirables ou de les détecter.

Increased Distance From the Tertiary Cardiac Center Is Associated With Worse 30-Day Outcomes After Cardiac Operations.

BACKGROUND: Numerous studies have examined the effect of geographic place of residence on access to cardiovascular care, but few have examined their effect on outcomes after cardiac operations. This study examined the effect of geographic place of residence on in-hospital and 30-day outcomes after cardiac operations. METHODS: We performed a retrospective analysis of all patients undergoing nonemergency cardiac operations at a single institution between April 2004 and March 2011. Geographic place of residence was defined as the driving distance from the patient's home to the tertiary cardiac care center divided into the following categories: 0 to 50 km, 50 to 100 km, 100 to 150 km, 150 to 200 km, 200 to 250 km, and more than 250 km. Multivariable logistic regression was used to determine the independent effect of driving distance on in-hospital and 30-day outcomes. RESULTS: The final study population included 4,493 patients, of whom 3,897 (86.7%) had 30-day follow-up. After adjusting for differences among patient groups, no consistent relationship existed between distance and in-hospital outcomes. However, increased distance beyond 100 km was significantly associated with a greater risk of adverse outcomes at 30 days (0 to 50 km: referent; 50 to 100 km: odds ratio, 1.16 [95% confidence interval, 0.83 to 1.62]; 100 to 150 km: 1.32 [1.05 to 1.65], 150 to 200 km: 1.68 [1.33 to 2.11], 200 to 250 km: 1.41 [1.06 to 1.88], and >250 km: 1.30 [1.04 to 1.63]). CONCLUSIONS: Patients who live at an increased distance from the tertiary cardiac care center are more likely to have worse 30-day outcomes after cardiac operations. Further study is required to determine the mechanisms underlying this relationship and how such inequalities may be minimized.

Factors related to dropout from treatment in two outpatient treatments for borderline personality disorder.

Patients with borderline personality disorder frequently drop out prematurely from psychotherapy. This study examined factors related to treatment attrition in 180 patients enrolled in a randomized controlled trial comparing 1 year of Dialectical Behavior Therapy (DBT) to General Psychiatric Management (GPM). Completers and dropouts were compared on a range of variables, including demographics, Axis I and Axis II disorders, anger and impulsivity, therapeutic alliance, and treatment condition. The participants were on average 30.36 years old and 86% were female. Regression analyses revealed that individuals who dropped out had higher levels of anger (p = .01), greater Axis I comorbidity (p = .03), poorer therapeutic alliance (p = .003), and a higher number of lifetime suicide attempts (p = .05). An interaction was also found between Axis I comorbidity and treatment condition, with significantly lower rates of dropout seen in individuals with high Axis I comorbidity who were assigned to GPM compared to those assigned to DBT (p < .001).

Gender roles in persistent sex differences in health-related quality-of-life outcomes of patients with coronary artery disease.

BACKGROUND: The increased recognition of significant sex/gender differences in health status outcomes, and the implications for clinical practice and service delivery, has led to calls for more gender sensitivity and specificity in research endeavors as well as within clinical practice. Previous investigations by our research group have consistently identified important sex differences in both changes in health status from baseline to 1 year and in health status outcomes of patients treated for coronary artery disease (CAD), with women reporting poorer health-related quality of life (HRQoL) compared with men. OBJECTIVE: The objective of this study was to examine whether persistent sex differences in the health status of patients with CAD may be attributed to social factors such as gender roles. METHODS: Sex differences in baseline clinical and demographic characteristics of patients who completed the 1-year follow-up survey were examined using t tests and χ(2) analyses. Structural equation modeling, an inclusive statistical modeling approach for testing hypotheses about relationships among measured and latent variables (concepts not observed or measured directly), was used to test our theoretical model. RESULTS: HRQoL data were collected on 2403 patients 1 year after index catheterization. The results indicated that the model fit was substantially improved by the addition of the conceptualized gender-role variable. Furthermore, there was a significant effect of gender role on QoL (-0.106; P < 0.05). Age, coronary anatomy, ejection fraction, physical limitation, anginal frequency, and gender role variables in this model were able to explain 51% of the variance in HRQoL. In particular, reported physical limitations, anginal frequency, and gender role had large statistically significant direct effects on HRQoL. CONCLUSIONS: Advances in the treatment of CAD have led to significant decreases in mortality rates. Our current challenge is to minimize the long-term impact of CAD on HRQoL outcomes. While a substantial body of literature has examined the correlations between gender-role attributes and a wide variety of both positive and negative outcomes, this area has not been explored in patients with cardiovascular disease. These findings suggest that further study of the influence of gender role (using a gender-role measurement) on HRQoL is needed.