Burn patients are at high risk of central line–associated bloodstream infection (CLABSI). However, the diagnosis of such infections is complex, resource-intensive, and often delayed. This study aimed to investigate the epidemiology of CLABSI and develop a prediction model for the infection in burn patients. The study analysed the infection profiles, clinical epidemiology, and central venous catheter (CVC) management of patients in a large burn centre in China from January 2018 to December 2021. In total, 222 burn patients with a cumulative 630 CVCs and 5,431 line-days were included. The CLABSI rate was 23.02 CVCs per 1000 line-days. The three most common bacterial species were Acinetobacter baumannii, Staphylococcus aureus, and Pseudomonas aeruginosa; 76.09% of isolates were multidrug resistant. Compared with a non-CLABSI cohort, CLABSI patients were significantly older, with more severe burns, more CVC insertion times, and longer total line-days, as well as higher mortality. Regression analysis found longer line-days, more catheterisation times, and higher burn wounds index to be independent risk factors for CLABSI. A novel nomogram based on three risk factors was constructed with an area under the receiver operating characteristic curve (AUROC) value of 0.84 (95% CI: 0.782–0.898) with a mean absolute error of calibration curve of 0.023. The nomogram showed excellent predictive ability and clinical applicability, and provided a simple, practical, and quantitative strategy to predict CLABSI in burn patients.

This study aimed to screen factors related to live birth outcomes of women with first frozen embryo transfer (FET). The enrolled women were divided into training and validation cohorts. The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning and the multiple regression model were then used to screen factors relevant to live birth failure (LBF) for the training dataset. A nomogram risk prediction model was established on the basis of the screened factors, and the consistency index (C-index) and calibration curve were derived for evaluating the model. The validation cohort was utilized to validate the nomogram model further. In total, 2083 women who accepted the first FET in our hospital were included and 44 factors were initially screened in this study. On the basis of the training cohort, the screened risk factors via multiple regression analysis with odds ratio (OR) values were female age (OR: 3.092, 95%CI: 1.065–4.852), body mass index (BMI; OR: 1.106, 95%CI: 1.015–1.546), caesarean section (OR: 1.909, 95%CI: 1.318–2.814), number of high-quality embryos (OR: 0.698, 95%CI: 0.599–0.812), and endometrial thickness (OR: 0.957, CI: 0.904–0.980). The nomogram model was generated based on five predictors. Furthermore, favourable results with C-indexes and calibration curves close to ideal curves indicated the accurate predictive ability of the nomogram. Female age, BMI, caesarean section, number of high-quality embryos, and endometrial thickness were independent predictors for LBF. The five factors of the risk assessment model may help to identify LBF with high accuracy in women who accept FET.

This retrospective study investigated the predictive value of the Controlling Nutritional Status (CONUT) score in patients with intermediate-stage hepatocellular carcinoma (HCC) who received transarterial chemoembolization (TACE). Nomograms were developed to predict progression-free and overall survival (PFS, OS). The medical data of 228 patients with HCC and treated with TACE were collected. The patients were apportioned to 2 groups according to CONUT score: low or high (<4, ≥4). Univariate and multivariate analyses were performed using Cox regression for OS and PFS. OS and PFS were estimated by the Kaplan-Meier curve and compared with the log-rank test. Nomograms were constructed to predict patient OS and PFS. The nomograms were evaluated for accuracy, discrimination, and efficiency. The cut-off value of CONUT score was 4. The higher the CONUT score, the worse the survival; Kaplan-Meier curves showed significant differences in OS and PFS between the low and high CONUT score groups (P = 0·033, 0·047). The nomograms including CONUT, based on the prognostic factors determined by the univariate and multivariate analyses, to predict survival in HCC after TACE were generated. The CONUT score is an important prognostic factor for both OS and PFS for patients with intermediate HCC who underwent TACE. The cut-off value of the CONUT score was 4. A high CONUT score suggests poor survival outcomes. Nomograms generated based on the CONUT score were good models to predict patient OS and PFS.

Coronavirus disease-2019 (COVID-19) elicits a range of different responses in patients and can manifest into mild to very severe cases in different individuals, depending on many factors. We aimed to establish a prediction model of severe risk in COVID-19 patients, to help clinicians achieve early prevention, intervention and aid them in choosing effective therapeutic strategy. We selected confirmed COVID-19 patients who were admitted to First Hospital of Changsha city between 29 January and 15 February 2020 and collected their clinical data. Multivariate logical regression was used to identify the factors associated with severe risk. These factors were incorporated into the nomogram to establish the model. The ROC curve, calibration plot and decision curve were used to assess the performance of the model. A total of 228 patients were enrolled and 33 (14.47%) patients developed severe pneumonia. Univariate and multivariate analysis showed that shortness of breath, fatigue, creatine kinase, lymphocytes and h CRP were independent factors for severe risk in COVID-19 patients. Incorporating age, chronic obstructive pulmonary disease (COPD) and these factors, the nomogram achieved good concordance indexes of 0.89 [95% confidence interval (CI) 0.832–0.949] and well-fitted calibration plot curves (Hosmer–Lemeshow test: P = 0.97). The model provided superior net benefit when clinical decision thresholds were between 15% and 85% predicted risk. Using the model, clinicians can intervene early, improve therapeutic effects and reduce the severity of COVID-19, thus ensuring more targeted and efficient use of medical resources.

The aim of this study was to develop and externally validate a simple-to-use nomogram for predicting the survival of hospitalised human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) patients (hospitalised person living with HIV/AIDS (PLWHAs)). Hospitalised PLWHAs (n = 3724) between January 2012 and December 2014 were enrolled in the training cohort. HIV-infected inpatients (n = 1987) admitted in 2015 were included as the external-validation cohort. The least absolute shrinkage and selection operator method was used to perform data dimension reduction and select the optimal predictors. The nomogram incorporated 11 independent predictors, including occupation, antiretroviral therapy, pneumonia, tuberculosis, Talaromyces marneffei, hypertension, septicemia, anaemia, respiratory failure, hypoproteinemia and electrolyte disturbances. The Likelihood χ2 statistic of the model was 516.30 (P = 0.000). Integrated Brier Score was 0.076 and Brier scores of the nomogram at the 10-day and 20-day time points were 0.046 and 0.071, respectively. The area under the curves for receiver operating characteristic were 0.819 and 0.828, and precision-recall curves were 0.242 and 0.378 at two time points. Calibration plots and decision curve analysis in the two sets showed good performance and a high net benefit of nomogram. In conclusion, the nomogram developed in the current study has relatively high calibration and is clinically useful. It provides a convenient and useful tool for timely clinical decision-making and the risk management of hospitalised PLWHAs.

Warfarin dosing for thromboprophylaxis in post-operative patients is time-consuming. Warfarin-dosing nomograms can be used in post-operative arthroplasty patients, but warfarin requirements are lower in frail older people. We modified an existing post-arthroplasty nomogram to a frail-friendly version and evaluated its performance in a frail elderly post-orthopaedic surgery on a geriatric rehabilitation ward to determine if it would improve quality indicators for oral anticoagulation. On a geriatric rehabilitation unit, post-operative orthopaedic patients were assigned to either physician-adjusted warfarin dosing or the nursing-administered nomogram. The proportion of days within target INR values was significantly higher in the nomogram group (77%, 95% CI 74% to 81%) compared to the physician-adjusted group (53%, 95% CI 46% to 60%), with no major bleeding or thromboembolic complications. The number of warfarin-related telephone calls to physicians was significantly reduced by tenfold. Use of a frail-friendly nomogram improved quality and efficiency of patient care on a geriatric rehabilitation unit.