Although surgical site infection after craniotomy (SSI-CRAN) is a serious complication, risk factors for its development have not been well defined. We aim to identify the risk factors for developing SSI-CRAN in a large prospective cohort of adult patients undergoing craniotomy. A series of consecutive patients who underwent craniotomy at a university hospital from January 2013 to December 2015 were prospectively assessed. Demographic, epidemiological, surgical, clinical and microbiological data were collected. Patients were followed up in an active post-discharge surveillance programm e for up to one year after surgery. Multivariate analysis was carried out to identify independent risk factors for SSI-CRAN. Among the 595 patients who underwent craniotomy, 91 (15.3%) episodes of SSI-CRAN were recorded, 67 (73.6%) of which were organ/space. Baseline demographic characteristics were similar among patients who developed SSI-CRAN and those who did not. The most frequent causative Gram-positive organisms were Cutibacterium acnes (23.1%) and Staphylococcus epidermidis (23.1%), whereas Enterobacter cloacae (12.1%) was the most commonly isolated Gram-negative agent. In the univariate analysis the factors associated with SSI-CRAN were ASA score > 2 (48.4% vs. 35.5% in SSI-CRAN and no SSI-CRAN respectively, p = 0.025), extrinsic tumour (28.6% vs. 19.2%, p = 0.05), and re-intervention (4.4% vs. 1.4%, p = < 0.001). In the multivariate analysis, ASA score > 2 (AOR: 2.26, 95% CI: 1.32–3.87; p = .003) and re-intervention (OR: 8.93, 95% CI: 5.33–14.96; p < 0.001) were the only factors independently associated with SSI-CRAN. The risk factors and causative agents of SSI-CRAN identified in this study should be considered in the design of preventive strategies aimed to reduce the incidence of this serious complication.

Aim To compare patient acuity, nurse staffing and workforce, missed nursing care and patient outcomes among hospital unit‐clusters. Background Relationships among acuity, nurse staffing and workforce, missed nursing care and patient outcomes, are not completely understood. Method Descriptive design with data from four unit‐clusters: medical, surgical, combined and step‐down units. Descriptive statistics were used to compare acuity, nurse staffing coverage, education and expertise, missed nursing care, and selected nurse‐sensitive outcomes. Results Patient acuity in general (medical, surgical and combined) floors is similar to step‐down units, with an average of 5.6 required RN hours per patient day. In general wards, available RN hours per patient day reach only 50% of required RN hours to meet patient needs. Workforce measures are comparable among unit‐clusters, and average missed nursing care is 21%. Patient outcomes vary among unit‐clusters. Conclusion Patient acuity is similar among unit‐clusters, whilst nurse staffing coverage is halved in general wards. While RN education, expertise and missed care are comparable among unit‐clusters, mortality, skin injuries and risk of family compassion fatigue rates are higher in general wards. Implications for nursing management Nurse managers play a pivotal role in hustling policy‐makers to address structural understaffing in general wards, to maximize patient safety outcomes.

Measuring the impact of care complexity on health outcomes, based on psychosocial, biological and environmental circumstances, is important in order to detect predictors of early deterioration of inpatients. We aimed to identify care complexity individual factors associated with selected adverse events and in-hospital mortality. A multicenter, case-control study was carried out at eight public hospitals in Catalonia, Spain, from January 1, 2016 to December 31, 2017. All adult patients admitted to a ward or a step-down unit were evaluated. Patients were divided into the following groups based on the presence or absence of three adverse events (pressure ulcers, falls or aspiration pneumonia) and in-hospital mortality. The 28 care complexity individual factors were classified in five domains (developmental, mental-cognitive, psycho-emotional, sociocultural and comorbidity/complications). Adverse events and complexity factors were retrospectively reviewed by consulting patients’ electronic health records. Multivariate logistic analysis was performed to identify factors associated with an adverse event and in-hospital mortality. A total of 183,677 adult admissions were studied. Of these, 3,973 (2.2%) patients experienced an adverse event during hospitalization (1,673 [0.9%] pressure ulcers; 1,217 [0.7%] falls and 1,236 [0.7%] aspiration pneumonia). In-hospital mortality was recorded in 3,996 patients (2.2%). After adjustment for potential confounders, the risk factors independently associated with both adverse events and in-hospital mortality were: mental status impairments, impaired adaptation, lack of caregiver support, old age, major chronic disease, hemodynamic instability, communication disorders, urinary or fecal incontinence, vascular fragility, extreme weight, uncontrolled pain, male sex, length of stay and admission to a medical ward. High-tech hospital admission was associated with an increased risk of adverse events and a reduced risk of in-hospital mortality. The area under the ROC curve for both outcomes was > 0.75 (95% IC: 0.78–0.83). Several care complexity individual factors were associated with adverse events and in-hospital mortality. Prior identification of complexity factors may have an important effect on the early detection of acute deterioration and on the prevention of poor outcomes.

Aim To assess the ability of the patient main problem to predict acuity in adults admitted to hospital wards and step‐down units Background Acuity refers to the categorization of patients based on their required nursing intensity. The relationship between acuity and nurses clinical judgment on the patient problems, including their prioritization, is an underexplored issue. Method Cross‐sectional, multi‐centre study in a sample of 200,000 adults. Multivariate analysis of main problems potentially associated with acuity levels higher than acute was performed. Distribution of patients and outcomes differences among acuity clusters were evaluated. Results The main problems identified are strongly associated with patient acuity. The model exhibits remarkable ability to predict acuity (AUC, 0.814; 95% CI, 0.81‐0.816). Most patients (64.8%) match higher than acute categories. Significant differences in terms of mortality, hospital readmission and other outcomes are observed (p<0.005). Conclusion The patient main problem predicts acuity. Most in‐patients require more intensive than acute nursing care and their outcomes are adversely affected. Implications for nursing management Prospective measurement of acuity, considering nurses clinical judgments on the patient main problem is feasible and may contribute to support nurse management workforce planning and staffing decision‐making, to optimize patients, nurses and organizational outcomes.