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3,426 results for “patient care”
An Inpatient Advance Care Planning Intervention for Older Patients With Hematologic Malignancies
ClinicalTrials.gov study NCT05433090. IPD Sharing: YES. Countries: 1. Publications: 1.
A Comparative Effectiveness Trial of Optimal Patient-Centered Care
ClinicalTrials.gov study NCT02274688. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Prophylactic Noninvasive Ventilation in vs Postoperative Standard Care in High Risk Patients According to ARISCAT Score
ClinicalTrials.gov study NCT03629431. IPD Sharing: NO. Countries: 1. Publications: 1.
Medical Education to Improve Diabetes Care and Outcomes in Hospitalized Patients
ClinicalTrials.gov study NCT07108426. IPD Sharing: NO. Countries: 1. Publications: 9.
β-D-Glucan (BDG) Surveillance With Preemptive Anidulafungin vs. Standard Care for Invasive Candidiasis in Surgical Intensive Care Unit (SICU) Patients
ClinicalTrials.gov study NCT00672841. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The PainSMART Research Program: Evaluating a Pain Education Strategy for Patients Seeking Primary Care Physiotherapy
ClinicalTrials.gov study NCT06187428. IPD Sharing: YES. Countries: 1. Publications: 20.
A Personalized Self-care Support Program for Primary Care Patients With Diabetic Foot Ulcer
ClinicalTrials.gov study NCT06540170. IPD Sharing: NO. Countries: 1. Publications: 1.
Urine Tenofovir Point-of-care Test to Identify Patients in Need of ART Adherence Support (UTRA Study)
ClinicalTrials.gov study NCT05333679. IPD Sharing: YES. Countries: 1. Publications: 57.
Safety and Efficacy of Daclatasvir (BMS-790052) Plus Standard of Care (Pegylated-interferon Alpha-2b and Ribavirin) in Japanese Patients
ClinicalTrials.gov study NCT01016912. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Utility index and vision related quality of life in patients awaiting specialist eye care
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Could dementia be detected from UK primary care patients' records by simple automated methods earlier than by the treating physician? A retrospective case-control study - Extended Data
<p>Extended data for Article published in Wellcome Open Research (Appendices 1,2 & 3). </p> <p>Abstract of Article: </p> <p><strong>Background:</strong> Timely diagnosis of dementia is a policy priority in the United Kingdom (UK). Primary care physicians receive incentives to diagnose dementia; however, 33% of patients are still not receiving a diagnosis. We explored automating early detection of dementia using data from patients’ electronic health records (EHRs). We investigated: a) how early a machine-learning model could accurately identify dementia before the physician; b) if models could be tuned for dementia subtype; and c) what the best clinical features were for achieving detection.</p> <p><strong>Methods:</strong> Using EHRs from Clinical Practice Research Datalink in a case-control design, we selected patients aged >65y with a diagnosis of dementia recorded 2000-2012 (cases) and matched them 1:1 to controls; we also identified subsets of Alzheimer’s and vascular dementia patients. Using 77 coded concepts recorded in the 5 years before diagnosis, we trained random forest classifiers, and evaluated models using Area Under the Receiver Operating Characteristic Curve (AUC). We examined models by year prior to diagnosis, subtype, and the most important features contributing to classification.</p> <p><strong>Results:</strong> 95,202 patients (median age 83y; 64.8% female) were included (50% dementia cases). Classification of dementia cases and controls was poor 2-5 years prior to physician-recorded diagnosis (AUC range 0.55-0.65) but good in the year before (AUC: 0.84). Features indicating increasing cognitive and physical frailty dominated models 2-5 years before diagnosis; in the final year, initiation of the dementia diagnostic pathway (symptoms, screening and referral) explained the sudden increase in accuracy. No substantial differences were seen between all-cause dementia and subtypes.</p> <p><strong>Conclusions:</strong> Automated detection of dementia earlier than the treating physician may be problematic, if using only primary care data. Future work should investigate more complex modelling, benefits of linking multiple sources of healthcare data and monitoring devices, or contextualising the algorithm to those cases that the GP would need to investigate.</p>
Development and validation of a postoperative delirium prediction model for patients admitted to an intensive care unit in China: a prospective study
<p>Objectives: We aimed to develop <span class="il">and</span> validate <span class="il">a</span> <span class="il">postoperative</span> <span class="il">delirium</span> (POD) <span class="il">prediction</span> model for patients admitted to the intensive care unit (ICU).</p> <p>Design: <span class="il">A</span> prospective study was conducted.</p> <p>Setting: The study was conducted in the surgical, cardiovascular surgical, <span class="il">and</span> trauma surgical ICUs <span class="il">of</span> an affiliated hospital <span class="il">of</span> <span class="il">a</span> medical university in Heilongjiang Province, China.</p> <p>Participants: This study included 400 patients (≥18 years old) admitted to the ICU after surgery.</p> <p>Primary <span class="il">and</span> secondary outcome measures: The primary outcome measure was <span class="il">postoperative</span> <span class="il">delirium</span> assessment during ICU stay.</p> <p>Results: The model was developed using 300 consecutive ICU patients <span class="il">and</span> was validated using 100 patients from the same ICUs. The model was based on five risk factors: Physiological <span class="il">and</span> Operative Severity Score for the Enumeration <span class="il">of</span> Mortality <span class="il">and</span> Morbidity; acid-base disturbance; <span class="il">and</span> history <span class="il">of</span> coma, diabetes, or hypertension. The model had an area under the receiver operating characteristics curve <span class="il">of</span> 0.852 (95% confidence interval: 0.802–0.902), Youden index <span class="il">of</span> 0.5789, sensitivity <span class="il">of</span> 70.73%, <span class="il">and</span> specificity <span class="il">of</span> 87.16%. The Hosmer-Lemeshow goodness <span class="il">of</span> fit was 5.203 (P = 0.736). At <span class="il">a</span> cut-off <span class="il">of</span> 24.5%, the sensitivity <span class="il">and</span> specificity were 71% <span class="il">and</span> 69%, respectively.</p> <p>Conclusions: The model, which used readily available data, exhibited high predictive value regarding risk <span class="il">of</span> intensive care unit <span class="il">postoperative</span> <span class="il">delirium</span> (ICU-POD) at admission. Use <span class="il">of</span> this model may facilitate better implementation <span class="il">of</span> preventive treatments <span class="il">and</span> nursing measures.</p>
Data from: Inaccuracy of venous point-of-care glucose measurements in critically ill patients: a cross-sectional study
Introduction: Current guidelines and consensus recommend arterial and venous samples as equally acceptable for blood glucose assessment in point-of-care devices, but there is limited evidence to support this recommendation. We evaluated the accuracy of two devices for bedside point-of-care blood glucose measurements using arterial, fingerstick and catheter venous blood samples in ICU patients, and assessed which factors could impair their accuracy. Methods: 145 patients from a 41-bed adult mixed-ICU, in a tertiary care hospital were prospectively enrolled. Fingerstick, central venous (catheter) and arterial blood (indwelling catheter) samples were simultaneously collected, once per patient. Arterial measurements obtained with Precision PCx, and arterial, fingerstick and venous measurements obtained with Accu-chek Advantage II were compared to arterial central lab measurements. Agreement between point-of-care and laboratory measurements were evaluated with Bland-Altman, and multiple linear regression models were used to investigate interference of associated factors. Results: Mean difference between Accu-chek arterial samples versus central lab was 10.7 mg/dL (95% LA -21.3 to 42.7 mg/dL), and between Precision PCx versus central lab was 18.6 mg/dL (95% LA -12.6 to 49.5 mg/dL). Accu-chek fingerstick versus central lab arterial samples presented a similar bias (10.0 mg/dL) but a wider 95% LA (-31.8 to 51.8 mg/dL). Agreement between venous samples with arterial central lab was the poorest (mean bias 15.1 mg/dL; 95% LA -51.7 to 81.9). Hyperglycemia, low hematocrit, and acidosis were associated with larger differences between arterial and venous blood measurements with the two glucometers and central lab. Vasopressor administration was associated with increased error for fingerstick measurements. Conclusions: Sampling from central venous catheters should not be used for glycemic control in ICU patients. In addition, reliability of the two evaluated glucometers was insufficient. Error with Accu-chek Advantage II increases mostly with central venous samples. Hyperglycemia, lower hematocrit, acidosis, and vasopressor administration increase measurement error.
Data from: Screening primary-care patients forgoing health care for economic reasons
Background: Growing social inequities have made it important for general practitioners to verify if patients can afford treatment and procedures. Incorporating social conditions into clinical decision-making allows general practitioners to address mismatches between patients' health-care needs and financial resources. Objectives: Identify a screening question to, indirectly, rule out patients' social risk of forgoing health care for economic reasons, and estimate prevalence of forgoing health care and the influence of physicians' attitudes toward deprivation. Design: Multicenter cross-sectional survey. Participants: Forty-seven general practitioners working in the French–speaking part of Switzerland enrolled a random sample of patients attending their private practices. Main Measures: Patients who had forgone health care were defined as those reporting a household member (including themselves) having forgone treatment for economic reasons during the previous 12 months, through a self-administered questionnaire. Patients were also asked about education and income levels, self-perceived social position, and deprivation levels. Key Results: Overall, 2,026 patients were included in the analysis; 10.7% (CI95% 9.4-12.1) reported a member of their household to have forgone health care during the 12 previous months. The question "Did you have difficulties paying your household bills during the last 12 months" performed better in identifying patients at risk of forgoing health care than a combination of four objective measures of socio-economic status (gender, age, education level, and income) (R2=0.184 vs. 0.083). This question effectively ruled out that patients had forgone health care, with a negative predictive value of 96%. Furthermore, for physicians who felt powerless in the face of deprivation, we observed an increase in the odds of patients forgoing health care of 1.5 times. Conclusion: General practitioners should systematically evaluate the socio-economic status of their patients. Asking patients whether they experience any difficulties in paying their bills is an effective means of identifying patients who might forgo health care.
Data from: Are patients with cancer with sepsis and bacteraemia at a higher risk of mortality? A retrospective chart review of patients presenting to a tertiary care centre in Lebanon
Objective: Most sepsis studies have looked at the general population. The aim of this study is to report on the characteristics, treatment and hospital mortality of patients with cancer diagnosed with sepsis or septic shock. Setting: A single-centre retrospective study at a tertiary care centre looking at patients with cancer who presented to our tertiary hospital with sepsis, septic shock or bacteraemia between 2010 and 2015. Participants: 176 patients with cancer were compared with 176 cancer-free controls. Primary and secondary outcomes: The primary outcome of this study was the in hospital mortality in both cohorts. Secondary outcomes included patient demographics, emergency department (ED) vital signs and parameters of resuscitation along with laboratory work. Results: A total of 352 patients were analysed. The mean age at presentation for the cancer group was 65.39±15.04 years, whereas the mean age for the control group was 74.68±14.04 years (p<0.001). In the cancer cohort the respiratory system was the most common site of infection (37.5%) followed by the urinary system (26.7%), while in the cancer-free arm, the urinary system was the most common site of infection (40.9%). intravenous fluid replacement for the first 24 hours was higher in the cancer cohort. ED, intensive care unit and general practice unit length of stay were comparable in both the groups. 95 (54%) patients with cancer died compared with 75 (42.6%) in the cancer-free group. The 28-day hospital mortality in the cancer cohort was 87 (49.4%) vs 46 (26.1%) in the cancer-free cohort (p=0.009). Patients with cancer had a 2.320 (CI 95% 1.225 to 4.395, p=0.010) odds of dying compared with patients without cancer in the setting of sepsis. Conclusions: This is the first study looking at an in-depth analysis of sepsis in the specific oncology population. Despite aggressive care, patients with cancer have higher hospital mortality than their cancer-free counterparts while adjusting for all other variables.
Data from: Short-term and medium-term survival of critically ill patients with solid tumours admitted to the intensive care unit: a retrospective analysis
Objectives: Patients with cancer frequently require unplanned admission to the Intensive Care Unit (ICU). Our objectives were to assess hospital and 180-day mortality in patients with a non-haematological malignancy and unplanned ICU admission, and to identify which factors present on admission were the best predictors of mortality. Design: Retrospective review of all patients with a diagnosis of solid tumours following unplanned admission to the ICU between 1st August 2008 and 31st July 2012. Setting: Single centre tertiary care hospital in London (UK) Participants: 300 adult patients with non-haematological solid tumours requiring unplanned admission to the ICU. Interventions: None Primary and secondary outcomes: Hospital and 180-day survival Results: 300 patients were admitted to the ICU (median age 66.5 years; 61.7% male). Survival to hospital discharge and 180-days were 69% and 47.8%, respectively. Greater number of failed organ systems on admission was associated with significantly worse hospital survival (p<0.001) but not with 180-day survival (p=0.24). In multivariate analysis, predictors of hospital mortality were the presence of metastases [odds ratio (OR 1.97), 95% confidence interval (CI) 1.08-3.59], Acute Physiology and Chronic Health Evaluation II (APACHE II) score (OR 1.07, 95% CI 1.01-1.13) and a Glasgow Coma Scale score <7 on admission to ICU (OR 5.21, 95% CI 1.65-16.43). Predictors of worse 180-day survival were the presence of metastases (OR 2.82, 95% CI 1.57-5.06), APACHE II score (OR 1.07, 95% CI 1.01-1.13) and sepsis (OR 1.92, 95% CI 1.09-3.38). Conclusions: Short and medium-term survival in patients with solid tumours admitted to ICU is better than previously reported, suggesting that the presence of cancer alone should not be a barrier to ICU admission.
Data from: Missed opportunities for HIV testing among patients newly presenting for HIV care at a Swiss university hospital: a retrospective analysis
Objectives: To determine the frequency of missed opportunities (MOs) among patients newly-diagnosed with HIV, risk factors for presenting MOs, and the association between MOs and late presentation to care. Design: Retrospective analysis Setting: HIV outpatient clinic at a Swiss tertiary hospital Participants: Patients aged ≥18 years old newly presenting for HIV care between 2010 and 2015 Measures: Number of medical visits, up to five years preceding HIV diagnosis, at which HIV testing had been indicated, according to Swiss HIV testing recommendations. A visit at which testing was indicated but not performed was considered a MO for HIV testing. Results: Complete records were available for all 201 new patients of whom 51% were male and 33% from sub-Saharan Africa. Thirty patients (15%) presented with acute HIV infection while 119 patients (59%) were late presenters (LPs) (CD4 counts <350 cells/mm3 at diagnosis). Ninety-four patients (47%) had presented at least one MO, of whom 44 (47%) had multiple MOs. MOs were more frequent among individuals from sub-Saharan Africa, men who have sex with men, and patients under follow-up for chronic disease. MOs were less frequent in LPs than non-LPs (42.5% versus 57.5%, P = 0.03). Conclusions: At our centre, 47% of patients presented at least one MO. Whilst our late presentation rate is higher than the national figure of 49.8%, LPs were less likely to experience MOs, suggesting that these patients were diagnosed late through presenting late, rather than through being failed by our hospital. We conclude that, in addition to optimising physician-initiated testing, access to testing must be improved among patients unaware they are at HIV risk and who do not seek health care.
Data from: An exploratory study of staff perceptions of shift safety in the critical care unit and routinely available data on workforce, patient and organisational factors
<p><b>Objectives </b></p> <p>To explore: bedside professional reported (BPR) perceptions of safety in intensive care staff; and the relationships between BPR safety, staffing, patient and work environment characteristics.</p> <p><b>Design </b>An exploratory study of self-recorded staff perceptions of shift safety and routinely collected data.</p> <p><b>Setting </b>A large teaching hospital comprising 70 critical care beds.</p> <p><b>Participants </b>All clinical staff working in adult critical care.</p> <p><b>Interventions </b>Staff recorded whether their shift felt "safe, unsafe or very unsafe" for 29 consecutive days. We explored these perceptions and relationships between these and routine data on staffing, patient and environmental characteristics.</p> <p><b>Outcome measures</b> Relationships between BPR safety and staffing, patient and work environment characteristics.</p> <p><b>Results</b> 2836 BPR scores were recorded over 29 consecutive days (response rate 57.7%). Perceptions of safety varied between staff, including within the same shift. There was no correlation between perceptions of safety and two measures of staffing; care hours per patient day (r= 0.13 p=0.108) and Safecare Allocate (r= -0.19 p=0.013). We found a significant, positive relationship between perceptions of safety and the percentage of Level 3 (most severely ill) patients (r=0.32, p=0.0001). There was a significant inverse relationship between perceptions of safety and the percentage of Level 1 patients on a shift (r= -0.42, p=<0.0001). Perceptions of safety correlated negatively with increased numbers of patients (r= -0.44, p=0.0006) and higher percentage of patients located side rooms (r=0.63, p<0.0001). We found a significant relationship between perceptions of safety and the percentage of staff with a specialist critical care course (r=0.42. p=0.0001).</p> <p><b>Conclusion </b>Existing staffing models, which are primarily influenced by staff:patient ratios may not be sensitive to patient need. Other factors may be important drivers of staff perceptions of safety and should be explored further.</p> <p><b>Trial registration</b>- UK Health Research Authority approval was obtained (ID249248).</p>
Self-care activities in pediatric patients with type 1 diabetes mellitus
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Basic laboratory and extended hematological parameters and intensive care unit mortality in sepsis patients
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.