Clinical Nursing Research最新文献

The readiness of essential equipment like airway trolleys is critical in pediatric cardiac intensive care units (PCICUs). Kaizen, a Lean management principle, has been increasingly applied in healthcare to enhance efficiency and patient care. This study investigates the transformative impact of Kaizen principles on airway trolley preparation in a 10-bedded PCICU. A 12-month prospective observational study was conducted between January 1, 2024 and December 31, 2024. Kaizen was introduced in the system on July 1, 2024. Pre-and post-Kaizen data were compared to evaluate its impact on response times, preparation efficiency, error reduction, resource utilization, and nursing satisfaction. We demonstrated remarkable reduction in response times during pediatric cardiac emergencies, from 4.82 to 2.14 min, post-Kaizen (p < .001). Time spent on airway trolley preparation decreased significantly from 12.5 to 7.3 min (p < .001). The frequency of errors in preparation decreased significantly from 4.2 to 1.1 errors per month. Waste reduction was achieved through streamlined processes, with nurses reporting a 30% reduction in preparation time. Nursing staff expressed heightened confidence and preparedness during high-stress situations. The application of Kaizen principles significantly optimized airway trolley preparation processes, highlighting their potential for broader healthcare applications.

Pulse oximetry is a widely used, noninvasive method for estimating arterial oxygen saturation (SaO2). However, emerging evidence suggests that skin pigmentation may affect its accuracy, potentially leading to occult hypoxemia in individuals with darker skin tones. This systematic review examines the impact of skin pigmentation on pulse oximeter accuracy by comparing pulse oximetry (SpO2) readings with arterial blood gas-measured SaO2 across diverse populations. A systematic search of PubMed and Embase was conducted following PRISMA 2020 guidelines. Eligible studies included those comparing SpO2 to SaO2 while stratifying results by skin pigmentation or race/ethnicity. Data extraction focused on bias in SpO2 readings, study design, and population characteristics. Risk of bias was assessed using the QUADAS-2 tool. Forty-two studies met the inclusion criteria. Consistent evidence indicated that pulse oximeters overestimate SaO2 in individuals with darker skin tones, particularly at lower oxygen saturations. This overestimation may delay recognition of hypoxemia and critical interventions. Methodological variability was noted, including inconsistent racial classifications and skin tone assessment methods. Pulse oximeters exhibit a systematic bias in individuals with darker skin tones. Standardized skin pigmentation assessment and improved device calibration are needed to enhance accuracy and ensure equitable patient care.

Severe illness and intensive care treatment pose significant challenges not only for the patients but also for their relatives, known as post-intensive care syndrome in family members (PICS-F). Not much is known about psychosocial outcomes in relatives of former severely ill COVID-19 patients who were hospitalized under pandemic-related challenges. This study aimed to investigate long-term psychosocial outcomes of relatives of formerly hospitalized COVID-19 patients in relation to patient and relative characteristics. Longitudinal data on psychosocial outcomes of relatives of COVID-19 patients, admitted to the general ward or intensive care unit (ICU) in 2020 and enrolled in the multicenter prospective cohort NeNeSCo study, were collected via questionnaires, 9 and 15 months post-hospital discharge of the patient. Outcomes of interest were anxiety, depression, post-traumatic stress symptoms (PTSS), caregiver burden, and quality of life. In general, relatives scored high on PTSS, especially in the ICU group (22.5%). Relatives of ICU patients had higher levels of anxiety and caregiver burden than those of general ward patients. Over time, anxiety decreased while caregiver burden increased in the total group. Factors associated with less favorable outcomes in terms of anxiety, depression, PTSS, and caregiver burden were associated with both relative and patient variables, with relatives' passive coping showing the strongest association across all outcome variables and time. Admission to the ICU increased the level of anxiety in relatives, while patient cognitive complaints were predictive of more severe symptoms in relatives (anxiety, depression, and caregiver burden). In conclusion, nurses providing follow-up care should be aware of the impact of severe COVID-19 on the psychosocial outcomes of relatives, comparable to other severe conditions, and offer guidance, especially to those who will not seek help themselves. Early screening for and psychoeducation on the emotional consequences of severely ill patients can guide nurses in their supportive care.

Intensive care unit (ICU) survivors increasingly report new or worsening functional impairment at hospital discharge. Early risk identification models that include high-dimensional nursing data may improve the delivery of preventive interventions. This study aims to develop and validate models predicting functional impairment at hospital discharge (Activity Measure for Post Acute Care [AMPAC] score <18) using electronic health record (EHR) data from the first 48 h of ICU admission. We identified 799 sepsis survivors hospitalized in the ICU (April 2016-May 2020) from a Midwestern health system's data warehouse. We extracted demographics, illness severity, nursing assessments, and ICU interventions. Given the limited availability of real-world EHR data, we employed CTAB-GAN, a generative adversarial network, to synthesize training data, enabling more robust model development. After feature engineering, 53 of 99 features were selected. We trained an eXtreme Gradient Boosting (XGBoost) classification model and used SHapley Additive exPlanations (SHAP) analysis to identify key predictors. Model performance was evaluated using the area under the receiver operating characteristic curves (AUC). For the 24-h model, the most critical features were first documented AMPAC score, age, mobility level, Braden Scale score, and walking device, while the 48-h model added body mass index and sequential organ failure assessment (SOFA) score as key predictors. Leveraging these findings, lightweight models were constructed using only the most important (top 5/10) predictors, which achieved results comparable to the full predictor model, with AUCs of 0.83 (24 h) and 0.83 (48 h), respectively. Our model, which includes patient characteristics and nurse assessments, can identify patients during early ICU admission who are at high risk for functional impairment at hospital discharge. Our streamlined modeling approach highlights the potential for integration into EHR systems, providing a practical and efficient tool for clinical decision support while maintaining predictive accuracy.

With aging, female older adults experience biochemical changes such as drop in their sex hormones and biomarkers and often encounter stress, which can be manifested in psychological symptoms. Previous literature has confirmed that racial/ethnic differences exist in the interactive relationship between sex hormones, biomarkers, and psychological symptoms. Yet, the racial/ethnic differences in their interactive relationship have not yet been examined. This is a secondary data analysis using the cross-sectional data of Wave II (2010-2011) from the National Social Life, Health, and Aging Project (NSHAP), and included 1,228 female older adults without moderate to severe cognitive impairment. Moderated network analysis was conducted with race as a moderator to examine the interactive relationship among sex hormones, biomarkers, and psychological symptoms and to compare the differences between the White and non-White group. The White group had a more positive relationship between total hemoglobin and cognition (edge weight = 0.18; moderated edge weight = 0.22). The non-White group had a positive relationship between progesterone and anxiety (edge weight = 0.05; moderated edge weight = 0.04) and between estradiol and cognition (edge weight = 0.03; moderated edge weight = 0.03), both of which were not present in the White group. We found a small moderated effect of race, and the strength of relationship among sex hormones, biomarkers, and psychological symptoms was different between the White and non-White group. Our study offers important preliminary findings to understand the potential racial/ethnic disparities that exist among sex hormones, biomarkers, and psychological symptoms in female older adults and the need to take an interactive approach.

Advance care planning, involving goals-of-care and surrogate-designation conversations, is crucial for patient-centered care. However, determining the optimal timing and participants for these conversations remains challenging. This study explored the frequency, timing, and predictors of documenting two advance care planning elements, goals-of-care and surrogate-designation conversations, in clinical notes for patients with advanced illness. In this retrospective observational study, we leveraged high-dimensional data and natural language processing (NLP) to analyze clinical notes and predict the presence or absence of advance care planning conversations. We included notes for patients treated at a Midwestern United States hospital who had advanced chronic conditions and eventually passed away. We manually labeled a gold-standard dataset (n = 913 notes) for the presence or absence of advance care planning conversations at the note level, achieving excellent inter-annotator agreement (90.5%). Training and testing four NLP models to detect goals-of-care and surrogate-designation conversations revealed that a transformer-based model (Bidirectional Encoder Representations from Transformers [BERT]) achieved the highest accuracy, with an F1 score of 93.6. We then deployed the BERT model to a high-dimensional corpus of 247,241 notes for 4,341 patients and detected goals-of-care and surrogate-designation conversations in the records of 85% and 60% of patients, respectively. Temporal analysis revealed that goals-of-care and surrogate-designation conversations were first documented at medians 28 and 8 days before death, respectively. Patient characteristics and referral to specialty palliative care emerged as significant factors associated with documenting these conversations. Our findings demonstrate the potential of NLP, particularly Transformer-based models like BERT, to accurately detect goals-of-care and surrogate-designation conversations in clinical narratives. This study identified significant temporal patterns, including late documentation, and patient characteristics associated with these conversations. It highlights the value of high-dimensional data in enhancing our understanding of advance care planning and offers insights for improving patient-centered care in clinical settings. Future research should explore the integration of these models into clinical workflows to facilitate timely and effective advance care planning discussions.

Previous studies have focused on identifying risk factors for older adults receiving home healthcare services without considering vital signs. This may provide important information on deteriorating health conditions that may lead to hospitalization and/or emergency department (ED) visits. Thus, it is important to understand the relationship between vital signs and hospitalization and/or ED visits and critical vital sign points for mitigating the higher risks of hospitalization and/or ED visits. This secondary data analysis uses cross-sectional data from a large, urban home healthcare organization (n = 61,615). A generalized additive model was used to understand the nonlinear relationship between each vital sign and hospitalization and/or ED visits through three unadjusted and adjusted models, and to identify a critical vital sign point related to a higher risk of hospitalization and/or ED visits. A significant nonlinear relationship (effective degree of freedom >2.0) was found between systolic, diastolic blood pressure, heart rate, hospitalization, and/or ED visits. The critical inflection point for systolic blood pressure was 120.36 (SE 3.625, p < .001), diastolic blood pressure was 72.00 (SE 3.108, p < .001), and heart rate was 83.24 (SE 1.994, p = .052). Among all vital signs, the risk of hospitalization and/or ED visits sharply increased when an older adult's heart rate surpassed 83.24 bpm. Our findings reveal that vital signs may serve as a critical indicator of a patient's clinical condition, especially related to hospitalization and/or ED visit. Clinicians need to be cognizant of these critical thresholds for each vital sign and monitor any deviations from baseline to preempt adverse outcomes.

This study aimed to develop and validate a machine learning-based predictive model for assessing the risk of fear of childbirth in pregnant women during late pregnancy. A cross-sectional observational study was conducted from November 2022 to July 2023, involving 406 pregnant women. Six machine learning algorithms, including Lasso-assisted logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGB), support vector machine (SVM), Bayesian network (BN), and k-nearest neighbors (KNN), were used to construct the models with 10-fold cross-validation. The results showed that the XGB model achieved the best performance, with an area under the receiver operating characteristic curve (AUC) of 0.874, accuracy of 0.795, sensitivity of 0.764, and specificity of 0.878. The LR model also performed well (AUC = 0.873). Key predictors of fear of childbirth included pain catastrophizing, expectation for painless childbirth, childbirth delivery preferences, medication use during pregnancy, and use of birth-related apps. The LR model was used to create a nomogram for clinical use. These machine learning models can help healthcare professionals identify and intervene early in cases of fear of childbirth.

The COVID-19 pandemic necessitated a triad of therapies for patients: oxygen, nutrition, and patient positioning. In the progressive care units, patients were placed in a prone position while receiving continuous enteral nutrition (EN) to optimize healing and oxygenation. The study aimed to identify the rate of aspiration pneumonia in non-ventilated COVID-19 patients placed in a prone position while receiving continuous EN. This was a single-group, descriptive retrospective study. The study was conducted at a two-time Magnet^® designated academic medical and health science center in the Southwestern United States. The sample included 97 electronic health records (EHRs) of patients diagnosed with COVID-19, receiving continuous EN, and placed in a prone position from March 15, 2020 to June 1, 2022. Data were extracted from EHRs using ICD-10 codes, including patient demographics, EN frequency, gastric tube placement, patient positioning, and incidence of aspiration pneumonia. Descriptive statistics and non-parametric tests were used. The Kruskal-Wallis rank sum test and Fisher's exact test were employed for comparisons. Statistical significance was set at p ≤ .05. Out of 97 patients, 8 (8.25%) developed aspiration pneumonia. The majority of patients (75%) had post-pyloric feeding tubes. All patients who developed aspiration pneumonia had post-pyloric tubes. Placing COVID-19 patients in a prone position while receiving continuous EN may be a safe practice. Diligent nursing assessment is crucial to minimize aspiration risk and optimize patient outcomes.