TAILR (Nursing-Sensitive Events and Their Association With Individual Nurse Staffing Levels) Project: Protocol for an International Longitudinal Multicenter Study.

BACKGROUND: Nursing-sensitive events (NSEs) are common, accounting for up to 77% of adverse events in hospitalized patients (eg, fall-related harm, pressure ulcers, and health care-associated infections). NSEs lead to adverse patient outcomes and impose an economic burden on hospitals due to increased medical costs through a prolonged hospital stay and additional medical procedures. To reduce NSEs and ensure high-quality nursing care, appropriate nurse staffing levels are needed. Although the link between nurse staffing and NSEs has been described in many studies, appropriate nurse staffing levels are lacking. Existing studies describe constant staffing exposure at the unit or hospital level without assessing patient-level exposure to nurse staffing during the hospital stay. Few studies have assessed nurse staffing and patient outcomes using a single-center longitudinal design, with limited generalizability. There is a need for multicenter longitudinal studies with improved potential for generalizing the association between individual nurse staffing levels and NSEs. OBJECTIVE: This study aimed (1) to determine the prevalence, preventability, type, and severity of NSEs; (2) to describe individual patient-level nurse staffing exposure across hospitals; (3) to assess the effect of nurse staffing on NSEs in patients; and (4) to identify thresholds of safe nurse staffing levels and test them against NSEs in hospitalized patients. METHODS: This international multicenter study uses a longitudinal and observational research design; it involves 4 countries (Switzerland, Sweden, Germany, and Iran), with participation from 14 hospitals and 61 medical, surgery, and mixed units. The 16-week observation period will collect NSEs using systematic retrospective record reviews. A total of 3680 patient admissions will be reviewed, with 60 randomly selected admissions per unit. To be included, patients must have been hospitalized for at least 48 hours. Nurse staffing data (ie, the number of nurses and their education level) will be collected daily for each shift to assess the association between NSEs and individual nurse staffing levels. Additionally, hospital data (ie, type, teaching status, and ownership) and unit data (ie, service line and number of beds) will be collected. RESULTS: As of January 2024, the verification process for the plausibility and comprehensibility of patients' and nurse staffing data is underway across all 4 countries. Data analyses are planned to be completed by spring 2024, with the first results expected to be published in late 2024. CONCLUSIONS: This study will provide comprehensive information on NSEs, including their prevalence, preventability, type, and severity, across countries. Moreover, it seeks to enhance understanding of NSE mechanisms and the potential impact of nurse staffing on these events. We will evaluate within- and between-hospital variability to identify productive strategies to ensure safe nurse staffing levels, thereby reducing NSEs in hospitalized patients. The TAILR (Nursing-Sensitive Events and Their Association With Individual Nurse Staffing Levels) study will focus on the optimization of scarce staffing resources. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56262.

Developing a reflection and analysis tool (We-ReAlyse) for readmissions to the intensive care unit: A quality improvement project.

BACKGROUND: Readmissions to the intensive care unit are associated with poorer patient outcomes and health prognoses, alongside increased lengths of stay and mortality risk. To improve quality of care and patients' safety, it is essential to understand influencing factors relevant to specific patient populations and settings. A standardized tool for systematic retrospective analysis of readmissions would help healthcare professionals understand risks and reasons affecting readmissions; however, no such tool exists. PURPOSE: This study's purpose was to develop a tool (We-ReAlyse) to analyze readmissions to the intensive care unit from general units by reflecting on affected patients' pathways from intensive care discharge to readmission. The results will highlight case-specific causes of readmission and potential areas for departmental- and institutional-level improvements. METHOD: A root cause analysis approach guided this quality improvement project. The tool's iterative development process included a literature search, a clinical expert panel, and a testing in January and February 2021. RESULTS: The We-ReAlyse tool guides healthcare professionals to identify areas for quality improvement by reflecting the patient's pathway from the initial intensive care stay to readmission. Ten readmissions were analyzed by using the We-ReAlyse tool, resulting in key insights about possible root causes like the handover process, patient's care needs, the resources on the general unit and the use of different electronic healthcare record systems. CONCLUSIONS: The We-ReAlyse tool provides a visualization/objectification of issues related to intensive care readmissions, gathering data upon which to base quality improvement interventions. Based on the information on how multi-level risk profiles and knowledge deficits contribute to readmission rates, nurses can target specific quality improvements to reduce those rates. IMPLICATIONS FOR CLINICAL PRACTICE AND RESEARCH: With the We-ReAlyse tool, we have the opportunity to collect detailed information about ICU readmissions for an in-depth analysis. This will allow health professionals in all involved departments to discuss and either correct or cope with the identified issues. In the long term, this will allow continuous, concerted efforts to reduce and prevent ICU readmissions. To obtain more data for analysis and to further refine and simplify the tool, it may be applied to larger samples of ICU readmissions. Furthermore, to test its generalizability, the tool should be applied to patients from other departments and other hospitals. Adapting it to an electronic version would facilitate the timely and comprehensive collection of necessary information. Finally, the tool's emphasis comprises reflecting on and analyzing ICU readmissions, allowing clinicians to develop interventions targeting the identified problems. Therefore, future research in this area will require the development and evaluation of potential interventions.

Incidence and characteristics of adverse events in paediatric inpatient care: a systematic review and meta-analysis.

BACKGROUND: Adverse events (AEs) cause suffering for hospitalised children, a fragile patient group where the delivery of adequate timely care is of great importance. OBJECTIVE: To report the incidence and characteristics of AEs, in paediatric inpatient care, as detected with the Global Trigger Tool (GTT), the Trigger Tool (TT) or the Harvard Medical Practice Study (HMPS) method. METHOD: MEDLINE, Embase, Web of Science and Google Scholar were searched from inception to June 2021, without language restrictions. Studies using manual record review were included if paediatric data were reported separately. We excluded studies reporting: AEs for a specific disease/diagnosis/treatment/procedure, or deceased patients; study protocols with no AE outcomes; conference abstracts, editorials and systematic reviews; clinical incident reports as the primary data source; and studies focusing on specific AEs only. Methodological risk of bias was assessed using a tool based on the Quality Assessment Tool for Diagnostic Accuracy Studies 2. Primary outcome was the percentage of admissions with ≥1 AEs. All statistical analyses were stratified by record review methodology (GTT/TT or HMPS) and by type of population. Meta-analyses, applying random-effects models, were carried out. The variability of the pooled estimates was characterised by 95% prediction intervals (PIs). RESULTS: We included 32 studies from 44 publications, conducted in 15 countries totalling 33 873 paediatric admissions. The total number of AEs identified was 8577. The most common types of AEs were nosocomial infections (range, 6.8%-59.6%) for the general care population and pulmonary-related (10.5%-36.7%) for intensive care. The reported incidence rates were highly heterogeneous. The PIs for the primary outcome were 3.8%-53.8% and 6.9%-91.6% for GTT/TT studies (general and intensive care population). The equivalent PI was 0.3%-33.7% for HMPS studies (general care). The PIs for preventable AEs were 7.4%-96.2% and 4.5%-98.9% for GTT/TT studies (general and intensive care population) and 10.4%-91.8% for HMPS studies (general care). The quality assessment indicated several methodological concerns regarding the included studies. CONCLUSION: The reported incidence of AEs is highly variable in paediatric inpatient care research, and it is not possible to estimate a reliable single rate. Poor reporting standards and methodological differences hinder the comparison of study results.

Variation in detected adverse events using trigger tools: A systematic review and meta-analysis.

BACKGROUND: Adverse event (AE) detection is a major patient safety priority. However, despite extensive research on AEs, reported incidence rates vary widely. OBJECTIVE: This study aimed: (1) to synthesize available evidence on AE incidence in acute care inpatient settings using Trigger Tool methodology; and (2) to explore whether study characteristics and study quality explain variations in reported AE incidence. DESIGN: Systematic review and meta-analysis. METHODS: To identify relevant studies, we queried PubMed, EMBASE, CINAHL, Cochrane Library and three journals in the patient safety field (last update search 25.05.2022). Eligible publications fulfilled the following criteria: adult inpatient samples; acute care hospital settings; Trigger Tool methodology; focus on specialty of internal medicine, surgery or oncology; published in English, French, German, Italian or Spanish. Systematic reviews and studies addressing adverse drug events or exclusively deceased patients were excluded. Risk of bias was assessed using an adapted version of the Quality Assessment Tool for Diagnostic Accuracy Studies 2. Our main outcome of interest was AEs per 100 admissions. We assessed nine study characteristics plus study quality as potential sources of variation using random regression models. We received no funding and did not register this review. RESULTS: Screening 6,685 publications yielded 54 eligible studies covering 194,470 admissions. The cumulative AE incidence was 30.0 per 100 admissions (95% CI 23.9-37.5; I2 = 99.7%) and between study heterogeneity was high with a prediction interval of 5.4-164.7. Overall studies' risk of bias and applicability-related concerns were rated as low. Eight out of nine methodological study characteristics did explain some variation of reported AE rates, such as patient age and type of hospital. Also, study quality did explain variation. CONCLUSION: Estimates of AE studies using trigger tool methodology vary while explaining variation is seriously hampered by the low standards of reporting such as the timeframe of AE detection. Specific reporting guidelines for studies using retrospective medical record review methodology are necessary to strengthen the current evidence base and to help explain between study variation.

The association between nurse staffing and inpatient mortality: A shift-level retrospective longitudinal study.

BACKGROUND: Worldwide, hospitals face pressure to reduce costs. Some respond by working with a reduced number of nurses or less qualified nursing staff. OBJECTIVE: This study aims at examining the relationship between mortality and patient exposure to shifts with low or high nurse staffing. METHODS: 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. RESULTS: 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]. DISCUSSION AND IMPLICATIONS: 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.

BACKGROUND: 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.

Describing adverse events in medical inpatients using the Global Trigger Tool.

AIMS: The purpose of the study was to describe the type, prevalence, severity and preventability of adverse events (AEs) that affected hospitalised medical patients. We used the previously developed and validated Global Trigger Tool from the Institute for Healthcare Improvement. METHODS: Using an adapted version of the Global Trigger Tool, we conducted a retrospective chart review of adult patients hospitalised in five medical wards at a university hospital in Switzerland. We reviewed a random sample of 20 patients’ charts for a total study period of 12 months (September 2016 to August 2017). Two trained nurses searched independently for triggers and possible AEs. All AEs were further validated by a senior physician. The number of triggers and AEs detected, as well as the severity and preventability of each, was assessed and analysed using descriptive statistics. RESULTS: From a sample of 240 patient charts, we identified 1371 triggers and 336 AEs in 144 (60%) inpatients. This translates to an AE rate of 95.7 AEs per 1000 patient days. Most AEs (86.1%) caused temporary harm to the patient and required an intervention and/or prolonged hospitalisation. The estimated preventability of the in-hospital AEs was 29%. Healthcare-associated infections (25.8%) and neurological reactions (22.9%) were the most frequent AE types. CONCLUSION: We found that about two thirds of patients suffered from AEs with harm during hospitalisation. It is common knowledge that AEs occur in hospitals and that they have potentially harmful consequences for patients, as well as a strong economic impact. However, to adequately prioritise patient safety interventions, it is essential to explore the nature, prevalence, severity and preventability of AEs. This is not only beneficial for the patients, but also cost effective in terms of shorter hospital stays.

Trigger Tool-Based Automated Adverse Event Detection in Electronic Health Records: Systematic Review.

BACKGROUND: Adverse events in health care entail substantial burdens to health care systems, institutions, and patients. Retrospective trigger tools are often manually applied to detect AEs, although automated approaches using electronic health records may offer real-time adverse event detection, allowing timely corrective interventions. OBJECTIVE: The aim of this systematic review was to describe current study methods and challenges regarding the use of automatic trigger tool-based adverse event detection methods in electronic health records. In addition, we aimed to appraise the applied studies' designs and to synthesize estimates of adverse event prevalence and diagnostic test accuracy of automatic detection methods using manual trigger tool as a reference standard. METHODS: PubMed, EMBASE, CINAHL, and the Cochrane Library were queried. We included observational studies, applying trigger tools in acute care settings, and excluded studies using nonhospital and outpatient settings. Eligible articles were divided into diagnostic test accuracy studies and prevalence studies. We derived the study prevalence and estimates for the positive predictive value. We assessed bias risks and applicability concerns using Quality Assessment tool for Diagnostic Accuracy Studies-2 (QUADAS-2) for diagnostic test accuracy studies and an in-house developed tool for prevalence studies. RESULTS: A total of 11 studies met all criteria: 2 concerned diagnostic test accuracy and 9 prevalence. We judged several studies to be at high bias risks for their automated detection method, definition of outcomes, and type of statistical analyses. Across all the 11 studies, adverse event prevalence ranged from 0% to 17.9%, with a median of 0.8%. The positive predictive value of all triggers to detect adverse events ranged from 0% to 100% across studies, with a median of 40%. Some triggers had wide ranging positive predictive value values: (1) in 6 studies, hypoglycemia had a positive predictive value ranging from 15.8% to 60%; (2) in 5 studies, naloxone had a positive predictive value ranging from 20% to 91%; (3) in 4 studies, flumazenil had a positive predictive value ranging from 38.9% to 83.3%; and (4) in 4 studies, protamine had a positive predictive value ranging from 0% to 60%. We were unable to determine the adverse event prevalence, positive predictive value, preventability, and severity in 40.4%, 10.5%, 71.1%, and 68.4% of the studies, respectively. These studies did not report the overall number of records analyzed, triggers, or adverse events; or the studies did not conduct the analysis. CONCLUSIONS: We observed broad interstudy variation in reported adverse event prevalence and positive predictive value. The lack of sufficiently described methods led to difficulties regarding interpretation. To improve quality, we see the need for a set of recommendations to endorse optimal use of research designs and adequate reporting of future adverse event detection studies.

Not only stiffness, but also yield strength of the trabecular structure determined by non-linear µFE is best predicted by bone volume fraction and fabric tensor.

The micro-architecture of cancellous bone is considered a major determinant of the fracture risk. Yet, if morphometry tells about alterations of the trabecular network, its elastic behaviour is best described by bone volume fraction (BV/TV) and the fabric tensor, which gives the anisotropy of the trabecular structure. This remains to be proven for yield strength, the onset of bone failure. The microstructure of 126 samples extracted from femoral heads of two female subjects was evaluated on micro-computed tomography scans via 25 structural indices. Parameters such as plate and rod decomposition via ITS and textural analyses by ISV, similar to the trabecular bone score, were also examined. The degree of collinearity between indices was assessed. The indices considered sufficiently independent were included in multi-linear regression models predicting stiffness or yield strength measured via nonlinear micro finite element analyses. The models' accuracy was checked and the contributions of all explanatory variables to the prediction were compared. Our results show that BV/TV alone explained most of the predicted yield strength (76%) and stiffness (89%). BV/TV together with the fabric tensor explained more than 98% of both measures! The fabric tensor also had a larger impact on yield strength (23%) than on the stiffness predictions (9%). On the other hand, the predictive value of the other independent factors (Tb.Th.SD, Tb.Sp.SD, rTb.Th, RR.Junc.D, ISV) was negligible (<1%). In conclusion, just as stiffness, yield strength of femoral trabecular bone is also best explained by BV/TV and trabecular anisotropy, the latter being even more relevant in its post-elastic behaviour.

Bone volume fraction and fabric anisotropy are better determinants of trabecular bone stiffness than other morphological variables.

As our population ages, more individuals suffer from osteoporosis. This disease leads to impaired trabecular architecture and increased fracture risk. It is essential to understand how morphological and mechanical properties of the cancellous bone are related. Morphology-elasticity relationships based on bone volume fraction (BV/TV) and fabric anisotropy explain up to 98% of the variation in elastic properties. Yet, other morphological variables such as individual trabeculae segmentation (ITS) and trabecular bone score (TBS) could improve the stiffness predictions. A total of 743 micro-computed tomography (µCT) reconstructions of cubic trabecular bone samples extracted from femur, radius, vertebrae, and iliac crest were analyzed. Their morphology was assessed via 25 variables and their stiffness tensor (CFE) was computed from six independent load cases using micro finite element (µFE) analyses. Variance inflation factors were calculated to evaluate collinearity between morphological variables and decide upon their inclusion in morphology-elasticity relationships. The statistically admissible morphological variables were included in a multiple linear regression model of the dependent variable CFE. The contribution of each independent variable was evaluated (ANOVA). Our results show that BV/TV is the best determinant of CFE(r(2) adj = 0.889), especially in combination with fabric anisotropy (r(2) adj = 0.968). Including the other independent predictors hardly affected the amount of variance explained by the model (r(2) adj = 0.975). Across all anatomical sites, BV/TV explained 87% of the variance of the bone elastic properties. Fabric anisotropy further described 10% of the bone stiffness, but the improvement in variance explanation by adding other independent factors was marginal (<1%). These findings confirm that BV/TV and fabric anisotropy are the best determinants of trabecular bone stiffness and show, against common belief, that other morphological variables do not bring any further contribution. These overall conclusions remain to be confirmed for specific bone diseases and postelastic properties.