Development and validation of a nomogram for predicting high-burnout risk in nurses.

AIM: To develop a predictive model for high-burnout of nurses. DESIGN: A cross-sectional study. METHODS: This study was conducted using an online survey. Data were collected by the Chinese Maslach Burnout Inventory-General Survey (CMBI-GS) and self-administered questionnaires that included demographic, behavioural, health-related, and occupational variables. Participants were randomly divided into a development set and a validation set. In the development set, multivariate logistic regression analysis was conducted to identify factors associated with high-burnout risk, and a nomogram was constructed based on significant contributing factors. The discrimination, calibration, and clinical practicability of the nomogram were evaluated in both the development and validation sets using receiver operating characteristic (ROC) curve analysis, Hosmer-Lemeshow test, and decision curve analysis, respectively. Data analysis was performed using Stata 16.0 software. RESULTS: A total of 2750 nurses from 23 provinces of mainland China responded, with 1925 participants (70%) in a development set and 825 participants (30%) in a validation set. Workplace violence, shift work, working time per week, depression, stress, self-reported health, and drinking were significant contributors to high-burnout risk and a nomogram was developed using these factors. The ROC curve analysis demonstrated that the area under the curve of the model was 0.808 in the development set and 0.790 in the validation set. The nomogram demonstrated a high net benefit in the clinical decision curve in both sets. CONCLUSION: This study has developed and validated a predictive nomogram for identifying high-burnout in nurses. RELEVANCE TO CLINICAL PRACTICE: The nomogram conducted by our study will assist nursing managers in identifying at-high-risk nurses and understanding related factors, helping them implement interventions early and purposefully. REPORTING METHOD: The study adhered to the relevant EQUATOR reporting guidelines: TRIPOD Checklist for Prediction Model Development and Validation. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Validation and performance of three scoring systems for predicting primary non-function and early allograft failure after liver transplantation.

BACKGROUND: Primary non-function (PNF) and early allograft failure (EAF) after liver transplantation (LT) seriously affect patient outcomes. In clinical practice, effective prognostic tools for early identifying recipients at high risk of PNF and EAF were urgently needed. Recently, the Model for Early Allograft Function (MEAF), PNF score by King's College (King-PNF) and Balance-and-Risk-Lactate (BAR-Lac) score were developed to assess the risks of PNF and EAF. This study aimed to externally validate and compare the prognostic performance of these three scores for predicting PNF and EAF. METHODS: A retrospective study included 720 patients with primary LT between January 2015 and December 2020. MEAF, King-PNF and BAR-Lac scores were compared using receiver operating characteristic (ROC) and the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) analyses. RESULTS: Of all 720 patients, 28 (3.9%) developed PNF and 67 (9.3%) developed EAF in 3 months. The overall early allograft dysfunction (EAD) rate was 39.0%. The 3-month patient mortality was 8.6% while 1-year graft-failure-free survival was 89.2%. The median MEAF, King-PNF and BAR-Lac scores were 5.0 (3.5-6.3), -2.1 (-2.6 to -1.2), and 5.0 (2.0-11.0), respectively. For predicting PNF, MEAF and King-PNF scores had excellent area under curves (AUCs) of 0.871 and 0.891, superior to BAR-Lac (AUC = 0.830). The NRI and IDI analyses confirmed that King-PNF score had the best performance in predicting PNF while MEAF served as a better predictor of EAD. The EAF risk curve and 1-year graft-failure-free survival curve showed that King-PNF was superior to MEAF and BAR-Lac scores for stratifying the risk of EAF. CONCLUSIONS: MEAF, King-PNF and BAR-Lac were validated as practical and effective risk assessment tools of PNF. King-PNF score outperformed MEAF and BAR-Lac in predicting PNF and EAF within 6 months. BAR-Lac score had a huge advantage in the prediction for PNF without post-transplant variables. Proper use of these scores will help early identify PNF, standardize grading of EAF and reasonably select clinical endpoints in relative studies.

Development and Validation of a Nomogram for Predicting Sarcopenia in Community-Dwelling Older Adults.

OBJECTIVE: To establish and validate a nomogram that predicts the risk of sarcopenia for community-dwelling older residents. DESIGN: Retrospective study. SETTING AND PARTICIPANTS: A total of 1050 community-dwelling older adults. METHODS: Data from a survey of community-dwelling older residents (≥60 years old) in Hunan, China, from June to September 2019 were retrospectively analyzed. The survey included general demographic information, diet, and exercise habits. Sarcopenia diagnosis was according to 2019 Asian Working Group for Sarcopenia criteria. Participants were randomly divided into the development group and validation groups. Independent risk factors were screened by multivariate logistic regression analysis. Based on the independent risk factors, a nomogram model was developed to predict the risk of sarcopenia for community-dwelling older adults. Both in the development and validation sets, the discrimination, calibration, and clinical practicability of the nomogram were verified using receiver operating characteristic curve analysis, Hosmer-Lemeshow test, and decision curve analysis, respectively. RESULTS: Sarcopenia was identified in 263 (25.0%) participants. Age, body mass index, marital status, regular physical activity habit, uninterrupted sedentary time, and dietary diversity score were significant contributors to sarcopenia risk. A nomogram for predicting sarcopenia in community-dwelling older adults was developed using these factors. Receiver operating characteristic curve analysis showed that the area under the curve was 0.827 (95% CI 0.792-0.860) and 0.755 (95% CI 0.680-0.837) in the development and validation sets, respectively. The Hosmer-Lemeshow test yielded P values of .609 and .565, respectively, for the 2 sets. The nomogram demonstrated a high net benefit in the clinical decision curve in both sets. CONCLUSIONS AND IMPLICATIONS: This study developed and validated a risk prediction nomogram for sarcopenia among community-dwelling older adults. Sarcopenia risk was classified as low (<11%), moderate (11%-70%), and high (>70%). This nomogram provides an accurate visual tool to medical staff, caregivers, and older adults for prediction, early intervention, and graded management of sarcopenia.

Development of a Novel Clinical Prognostic Model for Patients With Angioimmunoblastic T-Cell Lymphoma.

In this study we aimed to identify a set of prognostic factors for angioimmunoblastic T-cell lymphoma (AITL) and establish a novel prognostic model. The clinical data of 64 AITL patients enrolled to the Fourth Hospital of Hebei Medical University (from 2012 Jan to 2017 May) were retrospectively analyzed. The estimated 5-year overall survival and progression-free survival of this cohort of patients were 45.8% and 30.8%, respectively. Univariate analysis showed that age > 60 years, performance status ≥2, Ann Arbor stage III/IV, lactate dehydrogenase > 250 U/L, serum albumin (ALB) < 30 g/l, Coombs test positive, and Ki-67 rate ≥ 70% were significantly associated with poor prognosis. Multivariate analysis demonstrated that age > 60 years, ALB < 30 g/l, Ki-67 rate ≥ 70%, and Coombs test positive were independent prognosis factors for AITL. Here a new prognostic model, named as AITLI, was constructed using the top 5 significant prognostic factors for AITL prognostic prediction. The AITL patients were stratified into 3 risk groups: low, intermediate, and high risk groups. The new prognostic model AITLI showed better performance in predicting prognosis than the International Prognostic Index (IPI) and the prognostic index for PTCL, not otherwise specified (PIT) that were wisely used to predict the outcome for patients with other subtypes of lymphoma.

Evaluation of nosocomial infection risk using APACHE II scores in the neurological intensive care unit.

To evaluate the feasibility and accuracy of using the Acute Physiology, Age and Chronic Health Evaluation II (APACHE II) scoring system for predicting the risk of nosocomial infection in the neurological intensive care unit (NICU), 216 patients transferred to NICU within 24hours of admission were retrospectively evaluated. Based on admission APACHE II scores, they were classified into three groups, with higher APACHE II scores representing higher infectious risk. The device utilization ratios and device-associated infection ratios of NICU patients were analyzed and compared with published reports on patient outcome. Statistical analysis of nosocomial infection ratios showed obvious differences between the high-risk, middle-risk and low-risk groups (p<0.05). The area under the receiver operating characteristic curve of the APACHE II model in predicting the risk of nosocomial infection was 0.81, which proved to be reliable and consistent with the expectation. In addition, we found statistical differences in the duration of hospital stay (patient-days) and device utilization (device-days) between different risk groups (p<0.05). Thus the APACHE II scoring system was validated in predicting the risk of nosocomial infection, duration of patient-days and device-days, and providing accurate assessment of patients' condition, so that appropriate prevention strategies can be implemented based on admission APACHE II scores.