91 research outputs found
The association between nurse staffing and inpatient mortality: A shift-level retrospective longitudinal study
No full textWorldwide, hospitals face pressure to reduce costs. Some respond by working with a reduced number of nurses or less qualified nursing staff.; This study aims at examining the relationship between mortality and patient exposure to shifts with low or high nurse staffing.; This longitudinal study used routine shift-, unit-, and patient-level data for three years (2015-2017) from one Swiss university hospital. Data from 55 units, 79,893 adult inpatients and 3646 nurses (2670 registered nurses, 438 licensed practical nurses, and 538 unlicensed and administrative personnel) were analyzed. After developing a staffing model to identify high- and low-staffed shifts, we fitted logistic regression models to explore associations between nurse staffing and mortality.; Exposure to shifts with high levels of registered nurses had lower odds of mortality by 8.7% [odds ratio 0.91 95% CI 0.89-0.93]. Conversely, low staffing was associated with higher odds of mortality by 10% [odds ratio 1.10 95% CI 1.07-1.13]. The associations between mortality and staffing by other groups was less clear. For example, both high and low staffing of unlicensed and administrative personnel were associated with higher mortality, respectively 1.03 [95% CI 1.01-1.04] and 1.04 [95% CI 1.03-1.06].; This patient-level longitudinal study suggests a relationship between registered nurses staffing levels and mortality. Higher levels of registered nurses positively impact patient outcome (i.e. lower odds of mortality) and lower levels negatively (i.e. higher odds of mortality). Contributions of the three other groups to patient safety is unclear from these results. Therefore, substitution of either group for registered nurses is not recommended
Longitudinal study of the variation in patient turnover and patient-to-nurse ratio: Descriptive analysis of a Swiss University Hospital
No full textBackground: Variations in patient demand increase the challenge of balancing high-quality nursing skill mixes against budgetary constraints. Developing staffing guidelines that allow high-quality care at minimal cost requires first exploring the dynamic changes in nursing workload over the course of a day. Objective: Accordingly, this longitudinal study analyzed nursing care supply and demand in 30-minute increments over a period of 3 years. We assessed 5 care factors: patient count (care demand), nurse count (care supply), the patient-to-nurse ratio for each nurse group, extreme supply-demand mismatches, and patient turnover (ie, number of admissions, discharges, and transfers). Methods: Our retrospective analysis of data from the Inselspital University Hospital Bern, Switzerland included all inpatients and nurses working in their units from January 1, 2015 to December 31, 2017. Two data sources were used. The nurse staffing system (tacs) provided information about nurses and all the care they provided to patients, their working time, and admission, discharge, and transfer dates and times. The medical discharge data included patient demographics, further admission and discharge details, and diagnoses. Based on several identifiers, these two data sources were linked. Results: Our final dataset included more than 58 million data points for 128,484 patients and 4633 nurses across 70 units. Compared with patient turnover, fluctuations in the number of nurses were less pronounced. The differences mainly coincided with shifts (night, morning, evening). While the percentage of shifts with extreme staffing fluctuations ranged from fewer than 3% (mornings) to 30% (evenings and nights), the percentage within "normal" ranges ranged from fewer than 50% to more than 80%. Patient turnover occurred throughout the measurement period but was lowest at night. Conclusions: Based on measurements of patient-to-nurse ratio and patient turnover at 30-minute intervals, our findings indicate that the patient count, which varies considerably throughout the day, is the key driver of changes in the patient-to-nurse ratio. This demand-side variability challenges the supply-side mandate to provide safe and reliable care. Detecting and describing patterns in variability such as these are key to appropriate staffing planning. This descriptive analysis was a first step towards identifying time-related variables to be considered for a predictive nurse staffing model.</p
Supplemental material for Bootstrap-based testing approaches for the assessment of the diagnostic accuracy of biomarkers subject to a limit of detection
No full textSupplementary Material for Bootstrap-based testing approaches for the assessment of the diagnostic accuracy of biomarkers subject to a limit of detection by Alba M Franco-Pereira, Christos T Nakas, Alexander B Leichtle and M Carmen Pardo in Statistical Methods in Medical Research</p
Supplementary Material SMMR(2) - Supplemental material for Bootstrap-based testing approaches for the assessment of the diagnostic accuracy of biomarkers subject to a limit of detection
No full textSupplemental material, Supplementary Material SMMR(2) for Bootstrap-based testing approaches for the assessment of the diagnostic accuracy of biomarkers subject to a limit of detection by Alba M Franco-Pereira, Christos T Nakas, Alexander B Leichtle and M Carmen Pardo in Statistical Methods in Medical Research</p
Deployment of an Automated Method Verification-Graphical User Interface (MV-GUI) Software
No full textClinical laboratories frequently conduct method verification studies to ensure that the process meets quality standards for its intended use, such as patient testing. They play a pivotal role in healthcare, but issues such as accurate statistical assessment and reporting of verification data often make these studies challenging. Missteps can lead to false conclusions about method performance, risking patient safety or leading to incorrect diagnoses. Despite a requirement for accredited labs to document method performance, existing solutions are often expensive and complex. Addressing these issues, we present Method Verification-Graphical User Interface (MV-GUI), a software package designed for ease of use. It is platform-independent, capable of statistical analysis, and generates accreditation-ready reports swiftly and efficiently. Users can input patient data from one or more .CSV files, and MV-GUI will produce comprehensive reports, including statistical comparison tables, regression plots, and Bland–Altman plots. While method validation, which establishes the performance of new diagnostic tools, remains a crucial concern for manufacturers, MV-GUI primarily streamlines the method verification process. The software aids both medical practitioners and researchers and is designed to be user-friendly, even for non-experienced users. Requiring no internet connection, MV-GUI can operate in restricted IT environments, making method verification widely accessible and efficient
S-100 B Concentrations Are a Predictor of Decreased Survival in Patients with Major Trauma, Independently of Head Injury.
No full textBACKGROUND
Major trauma remains one of the principle causes of disability and death throughout the world. There is currently no satisfactory risk assessment to predict mortality in patients with major trauma. The aim of our study is to examine whether S-100 B protein concentrations correlate with injury severity and survival in patients with major trauma, with special emphasis on patients without head injury.
METHODS
Our retrospective data analysis comprised adult patients admitted to our emergency department between 1.12. 2008 and 31.12 2010 with a suspected major trauma. S-100 B concentrations were routinely assessed in major trauma patients.
RESULTS
A total of 27.7% (378) of all patients had major trauma. The median ISS was 24.6 (SD 8.4); 16.6% (63/378) of the patients died. S-100 B concentrations correlated overall with the ISS (p<0.0001). Patients who died had significantly higher S-100 B concentrations than survivors (8.2 μg/l versus 2.2 μg/l, p<0.0001). Polytraumatised patients with and without head trauma did not differ significantly with respect to S-100 B concentration (3.2 μg/l (SD 5.3) versus 2.9 μg/l (SD 3.8), respectively, p = 0.63) or with respect to Injury Severity Score (24.8 (SD 8.6) versus 24.2 (SD 8.1), respectively, p = 0.56). S-100 B concentrations correlated negatively with survival (p<0.0001) in all patients and in both subgroups (p = 0.001 and p = 0.006, respectively).
CONCLUSIONS
S-100 concentrations on admission correlate positively with greater injury severity and decreased survival in major trauma patients, independently of the presence of a head injury. S-100 B protein levels at admission in patients with major trauma may therefore be used to assess outcome in all polytraumatised patients. These measurements should be subject to further evaluation
A comparative study of pattern recognition algorithms for predicting the inpatient mortality risk using routine laboratory measurements
No full textLaboratory tests are a common and relatively cheap way to assess the general health status of patients. Various publications showed the potential of laboratory measurements for predicting inpatient mortality using statistical methodologies. However, these efforts are basically limited to the use of logistic regression models. In the present paper we use anonymized data from about 40,000 inpatient admissions to the Inselspital in Bern (Switzerland) to evaluate the potential of powerful pattern recognition algorithms employed for this particular risk prediction. In addition to the age and sex of the inpatients, a set of 33 laboratory measurements, frequently available at the Inselspital, are used as basic variables. In a large empirical evaluation we demonstrate that recent pattern recognition algorithms (such as random forests, gradient boosted trees or neural networks) outperform the more traditional approaches based on logistic regression. Moreover, we show how the predictions of the pattern recognition algorithms, which cannot be directly interpreted in general, can be calibrated to output a meaningful probabilistic risk score
Machine Learning in Antibody Diagnostics for Inflammatory Bowel Disease Subtype Classification
No full textAntibody testing in inflammatory bowel disease (IBD) can add to diagnostic accuracy of the main subtypes Crohn’s disease (CD) and ulcerative colitis (UC). Whether modern modeling techniques such as supervised and unsupervised machine learning are of value for finer distinction of subtypes such as IBD-unclassified (IBD-U) is not known. We determined the antibody profile of 100 adult IBD patients from the Swiss IBD cohort study with known subtype (50 CD, 50 UC) as well as of 76 IBD-U patients. We included ASCA IgG and IgA, p-ANCA, MPO- and PR3-ANCA, and xANCA measurements for computing different antibody panels as well as machine learning models. The AUC of an optimized antibody panel was 85% (95%CI, 78–92%) to distinguish CD from UC patients. The antibody profile of IBD-U patients was closely related to UC. No specific antibody profile was predictive for IBD-U nor for re-classification. The panel diagnostic was in favor of UC reclassification prediction with a correct assignment rate of 69.2–73.1% depending on the cut-off applied. Supervised machine learning could not distinguish between CD, UC, and IBD-U. More so, unsupervised machine learning suggested only two distinct clusters as a likely number of IBD subtypes. Antibodies in IBD are supportive in confirming clinical determined subtypes CD and UC but have limited capacity to predict IBD-U and reclassification during follow-up. In terms of antibody profiles, IBD-U is not a distinct subtype of IBD
Accuracy and Calibration of Computational Approaches for Inpatient Mortality Predictive Modeling.
No full textElectronic Health Record (EHR) data can be a key resource for decision-making support in clinical practice in the "big data" era. The complete database from early 2012 to late 2015 involving hospital admissions to Inselspital Bern, the largest Swiss University Hospital, was used in this study, involving over 100,000 admissions. Age, sex, and initial laboratory test results were the features/variables of interest for each admission, the outcome being inpatient mortality. Computational decision support systems were utilized for the calculation of the risk of inpatient mortality. We assessed the recently proposed Acute Laboratory Risk of Mortality Score (ALaRMS) model, and further built generalized linear models, generalized estimating equations, artificial neural networks, and decision tree systems for the predictive modeling of the risk of inpatient mortality. The Area Under the ROC Curve (AUC) for ALaRMS marginally corresponded to the anticipated accuracy (AUC = 0.858). Penalized logistic regression methodology provided a better result (AUC = 0.872). Decision tree and neural network-based methodology provided even higher predictive performance (up to AUC = 0.912 and 0.906, respectively). Additionally, decision tree-based methods can efficiently handle Electronic Health Record (EHR) data that have a significant amount of missing records (in up to >50% of the studied features) eliminating the need for imputation in order to have complete data. In conclusion, we show that statistical learning methodology can provide superior predictive performance in comparison to existing methods and can also be production ready. Statistical modeling procedures provided unbiased, well-calibrated models that can be efficient decision support tools for predicting inpatient mortality and assigning preventive measures
S-100 B Concentrations Are a Predictor of Decreased Survival in Patients With Major Trauma Independently of Head Injury
No full text- …
