Wound infection is prevalent in home healthcare (HHC) and often leads to hospitalizations. However, none of the previous studies of wounds in HHC have used data from clinical notes. Therefore, the authors created a more accurate description of a patient's condition by extracting risk factors from clinical notes to build predictive models to identify a patient's risk of wound infection in HHC.The structured data (eg, standardized assessments) and unstructured information (eg, narrative-free text charting) were retrospectively reviewed for HHC patients with wounds who were served by a large HHC agency in 2014. Wound infection risk factors were identified through bivariate analysis and stepwise variable selection. Risk predictive performance of three machine learning models (logistic regression, random forest, and artificial neural network) was compared.A total of 754 of 54,316 patients (1.39%) had a hospitalization or ED visit related to wound infection. In the bivariate logistic regression, language describing wound type in the patient's clinical notes was strongly associated with risk (odds ratio, 9.94; P < .05). The areas under the curve were 0.82 in logistic regression, 0.75 in random forest, and 0.78 in artificial neural network. Risk prediction performance of the models improved (by up to 13.2%) after adding risk factors extracted from clinical notes.Logistic regression showed the best risk prediction performance in prediction of wound infection-related hospitalization or ED visits in HHC. The use of data extracted from clinical notes can improve the performance of risk prediction models.
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We aimed to create and validate a natural language processing algorithm to extract wound infection-related information from nursing notes. We also estimated wound infection prevalence in homecare settings and described related patient characteristics. In this retrospective cohort study, a natural language processing algorithm was developed and validated against a gold standard testing set. Cases with wound infection were identified using the algorithm and linked to Outcome and Assessment Information Set data to identify related patient characteristics. The final version of the natural language processing vocabulary contained 3914 terms and expressions related to the presence of wound infection. The natural language processing algorithm achieved overall good performance (F-measure = 0.88). The presence of wound infection was documented for 1.03% (n = 602) of patients without wounds, for 5.95% (n = 3232) of patients with wounds, and 19.19% (n = 152) of patients with wound-related hospitalisation or emergency department visits. Diabetes, peripheral vascular disease, and skin ulcer were significantly associated with wound infection among homecare patients. Our findings suggest that nurses frequently document wound infection-related information. The use of natural language processing demonstrated that valuable information can be extracted from nursing notes which can be used to improve our understanding of the care needs of people receiving homecare. By linking findings from clinical nursing notes with additional structured data, we can analyse related patients' characteristics and use them to develop a tailored intervention that may potentially lead to reduced wound infection-related hospitalizations.
Abstract Purpose To evaluate the efficacy of next-generation sequencing (NGS) in minimal-residual-disease (MRD) monitoring in Chinese patients with multiple myeloma (MM). Methods This study analyzed 60 Chinese MM patients. During MRD monitoring in these patients’ post-therapy, clonal immunoglobulin heavy chain (IGH) rearrangements were detected via NGS using LymphoTrack assays. MRD monitoring was performed using NGS or next-generation flow cytometry (NGF), and the results were compared. Additionally, the sensitivity and reproducibility of the NGS method were assessed. Results The MRD detection range of the NGS method was 10 –6 –10 –1 , which suggested good linearity, with a Pearson correlation coefficient of 0.985 and a limit of detection of 10 –6 . Intra- and inter-assay reproducibility analyses showed that NGS exhibited 100% reproducibility with low variability in clonal cells. At diagnosis, unique clones were found in 42 patients (70.0%) with clonal IGH rearrangements, which were used as clonality markers for MRD monitoring post-therapy. Comparison of NGS and NGF for MRD monitoring showed 79.1% concordance. No samples that tested MRD-positive via NGF were found negative via NGS, indicating the higher sensitivity of NGS. MRD could be detected using NGS in 6 of 7 samples before autologous hematopoietic stem-cell transplantation, and 5 of them tested negative post-transplantation. In contrast, the NGF method could detect MRD in only 1 sample pre-transplantation. Conclusion Compared with NGF, NGS exhibits higher sensitivity and reproducibility in MRD detection and can be an effective strategy for MRD monitoring in Chinese MM patients.
Abstract Background Certain types of cancer and treatment increase the risk of falls among cancer patients, particularly patients with hematologic cancer undergoing bone marrow transplant (BMT). Nurses are integral to preventing falls and maintaining patient safety. Understanding patients undergoing BMT fall risk factors may help nurses identify high fall risk patients and develop fall prevention interventions. Purpose This systematic review aims to identify risk factors for falls among hospitalized adult patients receiving BMT treatment. Methods Guided by the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses, a systematic review of the literature was conducted by searching databases PubMed and CINAHL. Study quality was evaluated using the Crowe Critical Appraisal Tool form (v1.4). Findings An initial search yielded 829 articles; six were included for final review after removing duplicates and screening for inclusion criteria: specific to patients undergoing BMT, measure fall outcome, in hospital, and original research. The identified risk factors include age of 65 and older, leukemia diagnosis, days of diarrhea, incontinence of urine or stool, increased pulse rate, muscle weakness, hypnotic, anxiolytic medication, recent steroid use, allogenic transplant, and post‐engraftment period. Conclusions Risk factors for falls among patients undergoing BMT are multifactorial and are related to muscle weakness, medication administration, pulse rate, type of transplant, age, engraftment period, and bathroom use. Implications for nursing Nurses providing care to patients undergoing BMT need to assess and increase nurse surveillance on allogeneic transplant patients, specifically those on anxiolytic, hypnotic, and steroid medications. Nurses providing care to patients undergoing BMT should implement more fall prevention strategies in patients undergoing BMT who develop diarrhea and urine or stool incontinence. Identifying specific patients undergoing BMT fall risk factors and applying multifaceted individualized fall prevention strategies has the potential to improve allogeneic transplant patient care and prevent fall‐related complications.
Abstract Objectives: The objectives of this study were (1) to develop and validate a simulation model to estimate daily probabilities of healthcare-associated infections (HAIs), length of stay (LOS), and mortality using time varying patient- and unit-level factors including staffing adequacy and (2) to examine whether HAI incidence varies with staffing adequacy. Setting: The study was conducted at 2 tertiary- and quaternary-care hospitals, a pediatric acute care hospital, and a community hospital within a single New York City healthcare network. Patients: All patients discharged from 2012 through 2016 (N = 562,435). Methods: We developed a non-Markovian simulation to estimate daily conditional probabilities of bloodstream, urinary tract, surgical site, and Clostridioides difficile infection, pneumonia, length of stay, and mortality. Staffing adequacy was modeled based on total nurse staffing (care supply) and the Nursing Intensity of Care Index (care demand). We compared model performance with logistic regression, and we generated case studies to illustrate daily changes in infection risk. We also described infection incidence by unit-level staffing and patient care demand on the day of infection. Results: Most model estimates fell within 95% confidence intervals of actual outcomes. The predictive power of the simulation model exceeded that of logistic regression (area under the curve [AUC], 0.852 and 0.816, respectively). HAI incidence was greatest when staffing was lowest and nursing care intensity was highest. Conclusions: This model has potential clinical utility for identifying modifiable conditions in real time, such as low staffing coupled with high care demand.
Background: Infections are a frequent cause of hospital (re)admissions for older adults receiving home health care (HHC) in the United States. However, previous investigators have likely underestimated the prevalence of infections leading to hospitalization due to limitations of identifying infections using Outcome and Assessment Information Set (OASIS), the standardized assessment tool mandated for all Medicare-certified HHC agencies. By linking OASIS data with inpatient data from the Medicare Provider Analysis and Review (MedPAR) file, we were able to better quantify infection hospitalization trends and subsequent mortality among HHC patients. Method: After stratification (by census region, ownership, and urban or rural location) and random sampling, our data set consisted of 2,258,113 Medicare beneficiaries who received HHC services between January 1, 2013, and December 31, 2018, from 1,481 Medicare-certified HHC agencies. The 60-day HHC episodes were identified in OASIS. Hospital transfers reported in OASIS were linked with corresponding MedPAR records. Our outcomes of interest were (1) hospitalization with infection present on admission (POA); (2) hospitalization with infection as the primary cause; and (3) 30-day mortality following hospitalization with infection as the primary cause. We identified bacterial (including suspected) infections based on International Classification of Disease, Ninth Revision (ICD-9) and ICD-10 codes in MedPAR. We classified infections by site: respiratory, urinary tract, skin/soft tissue, intravenous catheter-related, and all (including other or unspecified infection site). We also identified sepsis diagnoses. Result: From 2013 through 2018, the percentage of 60-day HHC episodes with 1 or more hospital transfers ranged from 15% to 16%. Approximately half of all HHC patients hospitalized had an infection POA. Over the 6 years studied, infection (any type) was the primary cause of hospitalization in more than a quarter of all transfers (25.86%–27.57%). The percentage of hospitalizations due to sepsis increased from 7.51% in 2013 to 11.49% in 2018, whereas the percentage of hospitalizations due to respiratory, urinary tract, or skin/soft-tissue infections decreased (p <0.001). Thirty-day mortality following a transfer due to infection ranged from 14.14% in 2013 to 14.98% in 2018; mortality rates were highest following transfers caused by sepsis (23.14%-26.51%) and respiratory infections (13.07%-14.27%). Conclusion: HHC is an important source of post-acute care for those aging in place. Our findings demonstrate that infections are a persistent problem in HHC and are associated with substantial 30-day mortality, particularly following hospitalizations caused by sepsis, emphasizing the importance of infection prevention in HHC. Effective policies to promote best practices for infection prevention and control in the home environment are needed to mitigate infection risk. Funding: No Disclosures: None
Ongoing economic and health system reforms in China have transformed nurse employment in Chinese hospitals. Employment of 'bianzhi' nurses, a type of position with state-guaranteed lifetime employment that has been customary since 1949, is decreasing while there is an increase in the contract-based nurse employment with limited job security and reduced benefits. The consequences of inequities between the two types of nurses in terms of wages and job-related benefits are unknown. This study examined current rates of contract-based nurse employment and the effects of the new nurse contract employment strategy on nurse and patient outcomes in Chinese hospitals.This cross-sectional study used geographically representative survey data collected from 2008 to 2010 from 181 hospitals in six provinces, two municipalities, and one autonomous region in China. Logistic regression models were used to estimate the association between contract-based nurse utilization, dissatisfaction among contract-based nurses, nurse intentions to leave their positions, and patient satisfaction, controlling for nurse, patient, and hospital characteristics.Hospital-level utilization of contract-based nurses varies greatly from 0 to 91%, with an average of 51%. Contract-based nurses were significantly more dissatisfied with their remuneration and benefits than 'bianzhi' nurses who have more job security (P <0.01). Contract-based nurses who were dissatisfied with their salary and benefits were more likely to intend to leave their current positions (P <0.01). Hospitals with high levels of dissatisfaction with salary and benefits among contract-based nurses were rated lower and less likely to be recommended by patients (P < 0.05).Our results suggest a high utilization of contract-based nurses in Chinese hospitals, and that the inequities in benefits between contract-based nurses and 'bianzhi' nurses may adversely affect both nurse and patient satisfaction in hospitals. Our study provides empirical support for the 'equal pay for equal work' policy emphasized by the China Ministry of Health's recent regulations, and calls for efforts in Chinese hospitals to eliminate the disparities between 'bianzhi' and contract-based nurses.
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