Background/Objectives: In hospital settings, the wide variability of acute and complex chronic conditions—among both adult and pediatric patients—requires advanced approaches to detect early signs of clinical deterioration and the risk of transfer to the intensive care unit (ICU). Nursing diagnoses (NDs), standardized representations of patient responses to actual or potential health problems, reflect nursing complexity. However, most studies have focused on the total number of NDs rather than the individual role each diagnosis may play in relation to outcomes such as ICU transfer. This study aimed to identify and rank the specific NDs most strongly associated with ICU transfers in hospitalized adult and pediatric patients. Methods: A retrospective, monocentric observational study was conducted using electronic health records from an Italian tertiary hospital. The dataset included 42,735 patients (40,649 adults and 2086 pediatric), and sociodemographic, clinical, and nursing data were collected. A random forest model was applied to assess the predictive relevance (i.e., variable importance) of individual NDs in relation to ICU transfers. Results: Among adult patients, the NDs most strongly associated with ICU transfer were Physical mobility impairment, Injury risk, Skin integrity impairment risk, Acute pain, and Fall risk. In the pediatric population, Acute pain, Injury risk, Sleep pattern disturbance, Skin integrity impairment risk, and Airway clearance impairment emerged as the NDs most frequently linked to ICU transfer. The models showed good performance and generalizability, with stable out-of-bag and validation errors across iterations. Conclusions: A prioritized ranking of NDs appears to be associated with ICU transfers, suggesting their potential utility as early warning indicators of clinical deterioration. Patients presenting with high-risk diagnostic profiles should be prioritized for enhanced clinical surveillance and proactive intervention, as they may represent vulnerable populations.

Background/Objectives: Although nursing complexity and health literacy (HL) are critical determinants of patient outcomes, their combined impact on mortality, hospital re-admissions, and emergency department (ED) visits remains poorly understood. This study aims to measure nursing complexity and HL in hospitalized patients, examine their interaction, and analyze their impacts on mortality, hospital re-admissions, and ED visits over a one-year follow-up period. Methods: Adult patients from two hospital centers were enrolled, excluding those with stays under two days or cognitive impairments. Data were collected at baseline to assess nursing complexity (measured according to the number of nursing diagnoses assigned to patients within 24 h from hospital admission) and HL (assessed using the Single-Item Literacy Screener, SILS). Patients were followed during a 12-month follow-up period to track mortality, hospital re-admissions, and ED visits. Latent class analysis classified patients into distinct nursing complexity and HL profiles. Survival analyses and Cox proportional hazard models were used to evaluate the relationships between variables. Results: At baseline, among the 2667 enrolled patients, 55.9% were classified as having high nursing complexity, and 32% had inadequate HL. High nursing complexity was associated with lower HL (r = 0.384; p < 0.001). During follow-up, 387 patients (14.5%) were lost. Of the remaining sample, mortality occurred in 8.3% of the patients, hospital re-admissions in 27.2%, and ED visits in 16.8%. Nursing complexity was significantly associated with higher mortality (HR: 1.84, adjusted HR: 1.81), but not with hospital re-admissions or ED visits. The patients with inadequate HL (32%) had increased risks of mortality (HR: 11.21, adjusted HR: 7.75), hospital re-admissions (HR: 3.61, adjusted HR: 3.58), and ED visits (HR: 20.78, adjusted HR: 14.45). The patients with both high nursing complexity and inadequate HL had the highest mortality risk and the lowest 12-month survival rate (75%; 95% CI: 71.1–79.1%; p < 0.001). Conclusions: This study demonstrates that both high nursing complexity and inadequate HL independently and jointly contribute to adverse patient outcomes. Interventions targeting HL and supporting patients with high nursing complexity could reduce risks, enhance care, and improve patient survival. While these findings underscore the critical role of both factors in patient outcomes, the limitations include this study’s single-country setting and reliance on a single-item HL measure. Future research should validate these findings in broader healthcare contexts and integrate multidimensional HL assessments for a more comprehensive evaluation.

Background/Objectives: The increasing medical and nursing care complexity in hospitalized children represents a significant challenge for healthcare systems. However, the link between these two dimensions remains partially explored. This study aims to decipher the relationship between Diagnosis-Related Group (DRG) weight and nursing care complexity in hospitalized children and to identify the determinants of medical complexity. Methods: This retrospective study, conducted in an Italian university hospital, included children aged 2 to 11 years admitted to the hospital in 2022 with a minimum hospital stay of 2 days. Data were gathered from the Neonatal Pediatric Professional Assessment Instrument and the Hospital Discharge Register. DRG weight was used as an indicator of medical complexity, while the number of nursing diagnoses (NDs) documented in the first 24 h from hospital admission and the nursing actions (NAs) recorded during the patient’s hospital stay were used to measure nursing care complexity. Correlation analyses were conducted to explore the associations between DRG weight, NDs, and NAs. Stepwise regression was run to identify the key determinants of medical complexity across sociodemographic, clinical, organizational, and nursing variables. Results: Among 914 patients (mean age of 6.11 ± 2.90 years), the median DRG weight was 0.6982 (IQR: 0.5522). Patients had an average of 3.89 ± 2.83 NDs and a median of 17 NAs (IQR: 8). Significant correlations were found between NDs and NAs (r_s = 0.507; p < 0.001), as well as between DRG weight and the frequency of NDs (r_s = 0.232; p < 0.001) and NAs (r_s = 0.184; p < 0.001). Stepwise regression indicated that the number of NAs, surgical DRG, scheduled admissions, and ND frequency were significant determinants of DRG weight (R² = 0.311; adjusted R² = 0.308; p < 0.001). Conclusions: In children, DRG weight is also influenced by nursing care complexity, alongside clinical and organizational factors. An integrated approach is essential to enhance pediatric care and patient outcomes.

Background/Objectives: Rural–urban disparities are well documented in pediatric healthcare, influencing access, service organization, and outcomes. However, whether these differences extend to the domain of nursing care remains unclear. This study examined the association between degree of urbanization and nursing complexity among children admitted to a single tertiary-care pediatric hospital, comparing clinical, organizational, and nursing characteristics across geographic groups. Methods: Routinely collected electronic nursing documentation and hospital administrative records were retrospectively examined in a tertiary-care pediatric hospital in Rome, Italy. Children aged 2 to <18 years were consecutively included. Degree of urbanization was defined using the ISTAT-derived DEGURBA classification. The study outcome for nursing complexity corresponded to the number of nursing diagnoses documented at hospital admission. Descriptive analyses and a multivariable negative binomial regression model assessed the association between urbanization and nursing complexity, adjusting for age, sex, DRG weight, number of medical diagnoses, and chronic conditions. Results: Among 1652 pediatric hospitalizations, those from rural areas showed a higher clinical burden, with significantly higher DRG weight and more chronic conditions, but nursing complexity did not differ significantly across geographic groups. In the multivariable model, degree of urbanization was not independently associated with nursing complexity for admissions from towns and suburbs (IRR = 0.93, 95% CI 0.82–1.04, p = 0.211) or rural areas (IRR = 1.00, 95% CI 0.84–1.20, p = 0.966), compared with those from cities. Higher DRG weight (IRR = 1.11, 95% CI 1.05–1.17) and a greater number of medical diagnoses (IRR = 1.17, 95% CI 1.11–1.25) were positively associated with nursing complexity, whereas the number of chronic conditions showed an inverse association (IRR = 0.90, 95% CI 0.84–0.96). Conclusions: In this single-center sample, rural–urban differences in clinical burden are not accompanied by statistically detectable differences in nursing complexity at hospital admission. Despite a higher clinical burden among children admitted from less urbanized areas, nursing complexity does not appear to differ significantly across geographic groups. Standardized nursing data enhance the ability to compare admission-level care needs across geographic contexts, enabling more precise and stratified population and public health analyses.