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AimsPatients are harmed or die every year because of unsafe, inappropriate or inadequate healthcare delivery. Registered Nurses are a recognized patient safety strategy. However, variability in research findings indicate the relationship is not as simple as "more nurses=better outcomes." Hence, currently there exists no evidence-based frontline nursing care model. One emerging model is the Clinical Nurse Leader care model.DesignThis Hybrid Type II Implementation-Effectiveness study will evaluate the effect of the care model on standardized quality and safety outcomes and identify implementation characteristics that are sufficient and necessary to achieve outcomes.MethodsThis study leverages a natural experiment in 66 clinical care units in nine hospitals across five states in the United States that have implemented the Clinical Nurse Leader care model.ResultsFindings will elucidate Registered Nurse's mechanisms of action as organized into frontline models of care and link actions to improved care quality and safety.

The delivery and assessment of quality health care is complex with many interacting and interdependent components. In terms of research design and statistical analysis, this complexity and interdependency makes it difficult to assess the true impact of interventions designed to improve patient health care outcomes. Interrupted time series (ITS) is a quasi-experimental design developed for inferring the effectiveness of a health policy intervention while accounting for temporal dependence within a single system or unit. Current standardized ITS methods do not simultaneously analyze data for several units nor are there methods to test for the existence of a change point and to assess statistical power for study planning purposes in this context. To address this limitation, we propose the "Robust Multiple ITS" (R-MITS) model, appropriate for multiunit ITS data, that allows for inference regarding the estimation of a global change point across units in the presence of a potentially lagged (or anticipatory) treatment effect. Under the R-MITS model, one can formally test for the existence of a change point and estimate the time delay between the formal intervention implementation and the over-all-unit intervention effect. We conducted empirical simulation studies to assess the type one error rate of the testing procedure, power for detecting specified change-point alternatives, and accuracy of the proposed estimating methodology. R-MITS is illustrated by analyzing patient satisfaction data from a hospital that implemented and evaluated a new care delivery model in multiple units.

Primary care encounters are common among patients at risk for suicide. Prior suicide care programs that incorporate depression screening, suicide risk assessment, and collaborative safety planning show promise in nonprimary care settings. This secondary analysis of a stepped-wedge, cluster randomized implementation trial examines the effectiveness of a primary care–based suicide care program embedded alongside a substance use care program in reducing suicide attempts within 90 days of the primary care visit. Visual Abstract. Effectiveness of Integrating Suicide Care in Primary Care: Primary care encounters are common among patients at risk for suicide. Prior suicide care programs that incorporate depression screening, suicide risk assessment, and collaborative safety planning show promise in nonprimary care settings. This secondary analysis of a stepped-wedge, cluster randomized implementation trial examines the effectiveness of a primary care–based suicide care program embedded alongside a substance use care program in reducing suicide attempts within 90 days of the primary care visit. Background: Primary care encounters are common among patients at risk for suicide. Objective: To evaluate the effectiveness of implementing population-based suicide care (SC) in primary care for suicide attempt prevention. Design: Secondary analysis of a stepped-wedge, cluster randomized implementation trial. (ClinicalTrials.gov: NCT02675777) Setting: 19 primary care practices within a large health care system in Washington State, randomly assigned launch dates. Patients: Adult patients (aged ≥18 years) with primary care visits from January 2015 to July 2018. Intervention: Practice facilitators, electronic medical record (EMR) clinical decision support, and performance monitoring supported implementation of depression screening, suicide risk assessment, and safety planning. Measurements: Clinical practice and patient measures relied on EMR and insurance claims data to compare usual care (UC) and SC periods. Primary outcomes included documented safety planning after population-based screening and suicide risk assessment and suicide attempts or deaths (with self-harm intent) within 90 days of a visit. Mixed-effects logistic models regressed binary outcome indicators on UC versus SC, adjusted for randomization stratification and calendar time, accounting for repeated outcomes from the same site. Monthly outcome rates (percentage per 10 000 patients) were estimated by applying marginal standardization. Results: During UC, 255 789 patients made 953 402 primary care visits and 228 255 patients made 615 511 visits during the SC period. The rate of safety planning was higher in the SC group than in the UC group (38.3 vs. 32.8 per 10 000 patients; rate difference, 5.5 [95% CI, 2.3 to 8.7]). Suicide attempts within 90 days were lower in the SC group than in the UC group (4.5 vs. 6.0 per 10 000 patients; rate difference, −1.5 [CI, −2.6 to −0.4]). Limitation: Suicide care was implemented in combination with care for depression and substance use. Conclusion: Implementation of population-based SC concurrent with a substance use program resulted in a 25% reduction in the suicide attempt rate in the 90 days after primary care visits. Primary Funding Source: National Institute of Mental Health. [ABSTRACT FROM AUTHOR]

Current health policy calls for greater use of evidence-based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be "interrupted" by a change in a particular method of health care delivery. Interrupted time series (ITS) is a robust quasi-experimental design with the ability to infer the effectiveness of an intervention that accounts for data dependency. Current standardized methods for analyzing ITS data do not model changes in variation and correlation following the intervention. This is a key limitation since it is plausible for data variability and dependency to change because of the intervention. Moreover, present methodology either assumes a prespecified interruption time point with an instantaneous effect or removes data for which the effect of intervention is not fully realized. In this paper, we describe and develop a novel robust interrupted time series (robust-ITS) model that overcomes these omissions and limitations. The robust-ITS model formally performs inference on (1) identifying the change point; (2) differences in preintervention and postintervention correlation; (3) differences in the outcome variance preintervention and postintervention; and (4) differences in the mean preintervention and postintervention. We illustrate the proposed method by analyzing patient satisfaction data from a hospital that implemented and evaluated a new nursing care delivery model as the intervention of interest. The robust-ITS model is implemented in an R Shiny toolbox, which is freely available to the community.

Simple Summary: Advances in the treatment of acute lymphoblastic leukemia (ALL) have led to a marked improvement in the survival rate of patients. Nevertheless, these patients may develop adverse effects during and after treatment, such as bone abnormalities and vitamin D deficiency. Bone remodeling allows for bone volume and structure to be maintained, which is controlled by the receptor activator of the nuclear factor-kB (RANK)/RANK ligand (RANKL)/osteoprotegerin (OPG) system-determining pathway in the balance between bone formation and resorption. Some reports have explored the role of corticosteroids in modulating the RANKL and OPG levels and RANKL/OPG ratio in pediatric patients. Nevertheless, studies evaluating the role of RANKL and OPG in the bone health of pediatric ALL patients during treatment are limited. During remission, we observed an increase in the RANKL/OPG ratio, increased RANKL levels, and decreased OPG levels in ALL patients. These changes may predispose such patients to the development of bone health disorders in their adult lives. Background: The receptor activator of the nuclear factor-kB (RANK)/RANK ligand (RANKL)/osteoprotegerin (OPG) pathway is a determining pathway in the balance between bone formation and resorption, and disruptions in this complex can affect bone metabolism. Methods: This study analyzes the changes in RANKL, OPG, and 25(OH)D levels; the RANKL/OPG ratio; and other bone turnover markers (BTMs) from diagnosis to complete remission in children with acute lymphoblastic leukemia (ALL). This is a prospective observational cohort study, carried out at the Instituto Mexicano del Seguro Social, Mexico City, including 33 patients (4–17 years) with newly diagnosed B-cell ALL. The patients were treated with the HP09 chemotherapy protocol. Children who had previously been treated with corticosteroids were excluded. A peripheral blood sample at diagnosis and remission was collected to determine the 25(OH)D and BTM concentrations. Results: Increased RANKL (p = 0.001) and osteocalcin (p < 0.001) levels and RANKL/OPG ratio (<0.001) and a decreased OPG level (p = 0.005) were observed at remission, predominantly in the high-risk (HR) relapse and vitamin D deficiency groups. A negative association between RANKL and OPG (r = −0.454, p = 0.008) was observed. Conclusions: we suggest that the RANKL/OPG ratio could serve as a bone remodeling marker in ALL patients. [ABSTRACT FROM AUTHOR]

The logistic map is a nonlinear difference equation well studied in the literature, used to model self-limiting growth in certain populations. It is known that, under certain regularity conditions, the stochastic logistic map, where the parameter is varied according to a specified distribution, has a unique invariant distribution. In these cases we can compare the long-term behaviour of the stochastic system with that of the deterministic system evaluated at the average parameter value. Here we examine the relationship between the mean of the stochastic logistic equation and the mean of orbits of the deterministic logistic equation at the expected value of the parameter. We formally prove that, in some cases, the addition of noise is beneficial to the populations, in the sense that it increases the mean, while for other ranges of parameters it is detrimental. A conjecture based on numerical evidence is presented at the end. [ABSTRACT FROM AUTHOR]

The authors examined whether machine-learning models could be used to analyze data from electronic health records (EHRs) to predict patients' responses to antidepressant medications. EHR data from a Washington State health system identified patients ages ≥13 years who started an antidepressant medication in 2016 in a community practice setting and had a baseline Patient Health Questionnaire–9 (PHQ-9) score of ≥10 and at least one PHQ-9 score recorded 14–180 days later. Potential predictors of a response to antidepressants were extracted from the EHR and included demographic characteristics, psychiatric and substance use diagnoses, past psychiatric medication use, mental health service use, and past PHQ-9 scores. Random-forest and penalized regression analyses were used to build models predicting follow-up PHQ-9 score and a favorable treatment response (≥50% improvement in score). Among 2,469 patients starting antidepressant medication treatment, the mean±SD baseline PHQ-9 score was 17.3±4.5, and the mean lowest follow-up score was 9.2±5.9. Outcome data were available for 72% of the patients. About 48% of the patients had a favorable treatment response. The best-fitting random-forest models yielded a correlation between predicted and observed follow-up scores of 0.38 (95% CI=0.32–0.45) and an area under the receiver operating characteristic curve for a favorable response of 0.57 (95% CI=0.52–0.61). Results were similar for penalized regression models and for models predicting last PHQ-9 score during follow-up. Prediction models using EHR data were not accurate enough to inform recommendations for or against starting antidepressant medication. Personalization of depression treatment should instead rely on systematic assessment of early outcomes. [ABSTRACT FROM AUTHOR]

The peopling of the Americas has been the subject of extensive genetic, archaeological and linguistic research; however, central questions remain unresolved. One contentious issue is whether the settlement occurred by means of a single migration or multiple streams of migration from Siberia. The pattern of dispersals within the Americas is also poorly understood. To address these questions at a higher resolution than was previously possible, we assembled data from 52 Native American and 17 Siberian groups genotyped at 364,470 single nucleotide polymorphisms. Here we show that Native Americans descend from at least three streams of Asian gene flow. Most descend entirely from a single ancestral population that we call 'First American'. However, speakers of Eskimo-Aleut languages from the Arctic inherit almost half their ancestry from a second stream of Asian gene flow, and the Na-Dene-speaking Chipewyan from Canada inherit roughly one-tenth of their ancestry from a third stream. We show that the initial peopling followed a southward expansion facilitated by the coast, with sequential population splits and little gene flow after divergence, especially in South America. A major exception is in Chibchan speakers on both sides of the Panama isthmus, who have ancestry from both North and South America.

The authors aimed to use health records data to examine how the accuracy of statistical models predicting self-harm or suicide changed between 2015 and 2019, as health systems implemented suicide prevention programs. Data from four large health systems were used to identify specialty mental health visits by patients ages ≥11 years, assess 311 potential predictors of self-harm (including demographic characteristics, historical risk factors, and index visit characteristics), and ascertain fatal or nonfatal self-harm events over 90 days after each visit. New prediction models were developed with logistic regression with LASSO (least absolute shrinkage and selection operator) in random samples of visits (65%) from each calendar year and were validated in the remaining portion of the sample (35%). A model developed for visits from 2009 to mid-2015 showed similar classification performance and calibration accuracy in a new sample of about 13.1 million visits from late 2015 to 2019. Area under the receiver operating characteristic curve (AUC) ranged from 0.840 to 0.849 in the new sample, compared with 0.851 in the original sample. New models developed for each year for 2015–2019 had classification performance (AUC range 0.790–0.853), sensitivity, and positive predictive value similar to those of the previously developed model. Models selected similar predictors from 2015 to 2019, except for more frequent selection of depression questionnaire data in later years, when questionnaires were more frequently recorded. A self-harm prediction model developed with 2009–2015 visit data performed similarly when applied to 2015–2019 visits. New models did not yield superior performance or identify different predictors. [ABSTRACT FROM AUTHOR]