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Diabetic foot (DF) is one of the most common and serious complications of diabetes. Effective self-management by patients can delay disease progression and improve quality of life. Digital intelligent technologies have emerged as advantageous in assisting patients with chronic diseases in self-management. However, the impact of digital intelligent technologies on self-management of patients with DF remains unclear. This systematic review aimed to determine the effects of digital intelligent interventions on self-management in patients with DF. A systematic literature search was conducted across PubMed, Web of Science, Embase, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, Cochrane Central Register of Controlled Trial, ProQuest, China National Knowledge Internet, WanFang, China Science and Technology Journal Database, and SinoMed up to February 6, 2025, to identify eligible articles. Randomized controlled trials (RCTs) that assessed the effects of digital intelligent interventions on self-management of patients with DF were included. In total, 2 researchers independently conducted literature screening, quality assessment, and data extraction. The Cochrane Risk of Bias 2.0 tool (revised version 2019) for RCTs was used to assess the quality of the studies. A qualitative synthesis was conducted on the extracted data. In total, 1079 articles were retrieved, and 18 RCTs were included. All studies were rated as having a high risk of bias. The digital intelligent interventions in the included studies varied in forms, components, and durations. The intervention forms included WeChat (Tencent Holdings Limited; 7/18, 39%), apps (4/18, 22%), electronic platforms (3/18, 17%), mixed interventions (3/18, 17%), and smartphone thermography (1/18, 6%). The intervention components included self-management education (17/18, 94%), blood glucose and foot condition monitoring (8/18, 44%), self-management supervision and follow-up (6/18, 33%), and other components like foot risk assessment, foot care reminders, visit reminders, and remote consultations. Intervention durations ranged from 5 weeks to 12 months, with the majority (10/18, 56%) lasting 6 months. Among the 18 included studies, 17 studies (17/18, 94%) indicated that, compared with routine care, digital intelligent interventions significantly improved the self-management behaviors of patients with DF, including diabetes control, foot care behaviors, and blood glucose monitoring. Only 1 study (1/18, 6%) showed that the effects of digital intelligent interventions were not significantly different from those of routine care. In this systematic review, evidence suggests that digital intelligent interventions can improve self-management behaviors and capabilities in patients with DF. However, due to the overall low quality of the included studies, current evidence should be interpreted and applied with caution. This field is still in the exploratory stage, with significant heterogeneity among different studies and a lack of consensus on intervention strategies, necessitating further exploration tailored to different populations. Future RCTs with large sample sizes and rigorous design are needed to develop high-quality evidence. PROSPERO CRD42024524473; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024524473.

Background The mortality rate of secondary bloodstream infection (BSI) derived from the intestinal colonization of carbapenem-resistant Klebsiella pneumoniae (CRKP) is extremely high. This investigation aimed at clarifying the risk factors and prognosis of BSIs resulting from the initial colonisation of CRKP. Methods In this retrospective, cross-sectional study, we analyzed the clinical data of 167 patients with CRKP colonization who received active screening during hospitalization at Zhejiang Provincial People’s Hospital from January 2019 to December 2021. The cohort consisted of 34 patients with BSI (CRKP BSI group) and 133 patients without BSI (No-BSI CRKP group).Logistic regression was employed to identify risk factors for progression from CRKP intestinal colonization to secondary BSI.Cox proportional hazards regression models were used to analyze independent risk factors for 28-day crude mortality from CRKP BSI. Results Multivariable analysis revealed that previous use of carbapenems (odds ratio [OR]:4.14, 95% confidence interval [CI]: 1.07–16.0, P  = 0.040), corticosteroid use (OR: 3.18, 95% CI: 1.16–8.74, P  = 0.025), and agranulocytosis (OR: 7.54, 95% CI: 2.09–27.2; P  = 0.002) were independent risk factors for BSI in patients with CRKP rectal colonization. The overall mortality rate for CRKP infection was 20.4% (34/167), and the crude 28-day mortality rate for CRKP BSI was 44.1% (15/34), which was independently associated with hematologic neoplasms ( P <  0.001). Among the 11 genotypically evaluated CRKP strains, 10 harbored the bla KPC−2 gene. Conclusions Neutrophil deficiency, previous use of carbapenems, and corticosteroid use are risk factors for BSI following CRKP colonization. Patients with hematologic neoplasms associated with CRKP infection are at high risk of death. Patients with clinical risk factors should be identified early, and targeted intervention measures should be taken to optimize antibiotic use and reduce the risk of subsequent BSI.

This quality improvement study analyzed factors influencing situational insomnia among perioperative patients in general surgical wards using a combination of literature review, questionnaire surveys, and group discussions. Guided by the Optimal Healing Environment (OHE) model, which emphasizes creating a healing atmosphere through internal, external, and interpersonal factors, we developed a multi-modal sleep management program. Key components included standardizing sleep assessment protocols, optimizing the environment through noise reduction strategies (e.g., noise sensors) and light control (e.g., light-reducing films), implementing staff behavior modifications such as “Quiet Hour” protocols, and providing patient and family education. Post-implementation, the incidence of situational insomnia decreased significantly from 66.4% to 35.5%, noise levels during midday and nighttime were reduced by 21.5% and 28.0%, respectively, and patient satisfaction with noise and temperature management improved by 38.7% and 20.3%, respectively. These results highlight the effectiveness of the OHE-based program in improving sleep quality, accelerating recovery, and enhancing patient satisfaction in general surgery settings.

Background This research aimed to estimate the prognostic value of the platelet-to-lymphocyte ratio (PLR) in colorectal cancer (CRC) patients receiving chemotherapy. Methods Embase, the Cochrane Library, Web of Science, and PubMed were searched from inception to March 20, 2025. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated to appraise the predictive significance of PLR among CRC patients receiving chemotherapy. The robustness of the findings and potential publication bias were assessed by sensitivity analysis and Egger’s test, respectively. The impact of confounding variables was investigated through subgroup and regression analyses. Results In total, 33 studies involving 7,529 individuals were included. Statistical analysis of categorical variables demonstrated that an increased PLR was significantly related to overall survival (OS) (HR = 1.33, 95% CI = 1.14–1.54), progression-free survival (PFS) (HR = 1.32, 95% CI = 1.06–1.63), and relapse-free survival (RFS) (HR = 1.77, 95% CI = 1.13–2.77) in CRC patients receiving chemotherapy, whereas no significant relationship was found with disease-free survival (DFS) (HR = 1.19, 95% CI = 0.84–1.69). Subgroup analyses indicated that, among metastatic CRC patients, a higher PLR was significantly linked to unfavorable OS and DFS. In CRC patients receiving adjuvant chemotherapy or chemotherapy with or without targeted therapy, high PLR was significantly related to worse OS and PFS. Conclusion An elevated PLR is related to adverse prognosis among CRC patients undergoing chemotherapy. In clinical practice, PLR may be a promising and cost-effective predictive marker for this population.

Aims/Introduction To explore the association between lactation and type 2 diabetes incidence in women with prior gestational diabetes. Materials and Methods We searched PubMed, Embase and the Cochrane Library for cohort studies published through 12 June 2017 that evaluated the effect of lactation on the development of type 2 diabetes in women with prior gestational diabetes. A random effects model was used to estimate relative risks (RRs) with 95% confidence intervals (CIs). Results A total of 13 cohort studies were included in the meta‐analysis. The pooled result suggested that compared with no lactation, lactation was significantly associated with a lower risk of type 2 diabetes (RR 0.66, 95% CI 0.48–0.90, I2 = 72.8%, P < 0.001). This relationship was prominent in a study carried out in the USA (RR 0.66, 95% CI 0.43–0.99), regardless of study design (prospective design RR 0.56, 95% CI 0.41–0.76; retrospective design RR 0.63, 95% CI 0.40–0.99), smaller sample size (RR 0.52, 95% CI 0.30–0.92, P = 0.024) and follow‐up duration >1 years (RR 0.75, 95% CI 0.56–1.00), and the study used adjusted data (RR 0.69, 95% CI 0.50–0.94). Finally, by pooling data from three studies, we failed to show that compared with no lactation, long‐term lactation (>1 to 3 months postpartum) was associated with the type 2 diabetes risk (RR 0.69, 95% CI 0.41–1.17). Conclusions The present meta‐analysis showed that lactation was associated with a lower risk of type 2 diabetes in women with prior gestational diabetes. Furthermore, no significant relationship between long‐term lactation and type 2 diabetes risk was detected. The impact of long‐term lactation and the risk of type 2 diabetes should be verified in further large‐scale studies. Flow diagram showing the study selection process.

Information distortion in nursing records poses significant risks to patient safety and impedes the enhancement of care quality. The introduction of information technologies, such as decision support systems and predictive models, expands the possibilities for using health data but also complicates the landscape of information distortion. Only by identifying influencing factors about information distortion can care quality and patient safety be ensured. This study aims to explore the factors influencing information distortion in electronic nursing records (ENRs) within the context of China's health care system and provide appropriate recommendations to address these distortions. This qualitative study used semistructured interviews conducted with 14 nurses from a Class-A tertiary hospital. Participants were primarily asked about their experiences with and observations of information distortion in clinical practice, as well as potential influencing factors and corresponding countermeasures. Data were analyzed using inductive content analysis, which involved initial preparation, line-by-line coding, the creation of categories, and abstraction. The analysis identified 4 categories and 10 subcategories: (1) nurse-related factors-skills, awareness, and work habits; (2) patient-related factors-willingness and ability; (3) operational factors-work characteristics and system deficiencies; and (4) organizational factors-management system, organizational climate, and team collaboration. Although some factors influencing information distortion in ENRs are similar to those observed in paper-based records, others are unique to the digital age. As health care continues to embrace digitalization, it is crucial to develop and implement strategies to mitigate information distortion. Regular training and education programs, robust systems and mechanisms, and optimized human resources and organizational practices are strongly recommended.

Background The amount of prediction models for disability in older adults is increasing but the prediction performance of different models varies greatly, and the quality of prediction models is still unclear. Objectives To systematically review and critically appraise the studies on risk prediction models for disability in older adults. Methods A systematic literature search was conducted on PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), and Wanfang Database, published up until June 30, 2023. Data were extracted according to the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability of the included studies. In addition, all included studies were evaluated for clinical value. Results A total of 5722 articles were initially retrieved from databases, 16 studies and 17 prediction models were finally included after screening. The sample sizes of studies ranged from 420 to 90,889. Model development methods mainly included logistic regression analysis, Cox proportional hazards regression, and machine learning methods. The C statistic or area under the curve (AUC) of models ranged from 0.650 to 0.853, and nine models had C statistic/AUC higher than 0.75. Age, chronic disease, gender, self-rated health, body mass index (BMI), drinking, smoking and education level were the most common predictors. According to the PROBAST, all included studies were at high risk of bias, and 10 studies were at high concerns for applicability. Only two studies reported following the Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement. After evaluation, only two models reached the standard of clinical value. Conclusion Although most of the included prediction models had acceptable discrimination, the overall quality and clinical value of the current studies were poor. In the future, researchers should follow the TRIPOD statement and PROBAST checklist to develop prediction models with larger sample sizes, more reasonable study designs, and more scientific analysis methods, to improve the predictive performance and application value. Trial registration The review protocol was registered in PROSPERO (registration ID: CRD42023446657).

Background Artificial intelligence (AI) has played an important role in the field of medical risk prediction with its strong learning ability and data processing capabilities. With the rapid development of research in this field, it is necessary to conduct quantitative literature analysis to understand the development trends and research hotspots of AI in the field of medical risk prediction. Objective Through a comprehensive bibliometric analysis, this paper summarizes the development stage and key research hotspots of the application of AI in the field of medical risk prediction in the past 20 years. Additionally, we provide a thorough analysis of emerging trends and future directions, offering insights into how advancements in AI are likely to shape risk prediction methodologies and their clinical applications in the years to come. Methods Relevant articles from the establishment of the database to 2024 were retrieved through Science Citation Index and Social Sciences Citation Index of the Web of Science Core Collection. Citespace, VOSviewer, Scimago Graphica, Pajek, and other software were used for bibliometric and visual analysis. Result A total of 2080 articles were included. From 1986 to 2004, this field experienced a slow development period, with the number of papers published per year less than 10. From 2005 to 2020, the number of papers published increased with a linear trend, and entered an exponential rapid growth stage after 2020, with the development entering a mature stage. The United States was the country with the most extensive cooperation and the largest number of publications (652 articles, 31.35%). The diseases, AI technologies, and functions that have received the most attention in this field are cancer, machine learning, and prediction, respectively. Conclusions Artificial intelligence in medical risk prediction has transitioned from technical exploration to a critical component of clinical practice, expanding from single-disease forecasts to complex, multimodal assessments. Advances in machine learning and personalized medicine have integrated AI into medical decision-making and management, yet widespread adoption requires addressing challenges related to interpretability, privacy, ethics, reliability, and standardization. In the future, AI is expected to significantly enhance prediction accuracy, optimize health management, and advance personalized medicine.

Background It is crucial to identify the risk factors for unplanned extubation (UEX) in thoracoabdominal drainage tubes as early as possible and establish applicable risk prediction model to reduce the incidence of UEX. Methods A retrospective survey of patients who underwent Thoracoabdominal drainage tubes placement at a tertiary hospital was conducted in Zhejiang Province, China, between January 2020 and January 2023. A training set was established to build the predictive model and conduct internal validation, which was assessed for discrimination using ROC curves and for Calibration using the Hosmer–Lemeshow test and Calibration curves. A nomogram was constructed to visually present the results of the logistic regression analysis. An external validation dataset was created for assessing the external validation of the model. Results a total of 2220 patients were enrolled. Multiple logistic regression analysis showed that negative pressure ball drainage, adhesive fixation method, self-care ability (self-care vs. complete dependence), self-care ability (partial dependence vs. complete dependence), and Thoracoabdominal drainage tubes were statistically significant factors associated with UEX ( P  < 0.05).The predictive model equation was as follows: a = 0.95–1.66 × drainage method + 2.45 × fixation method −4.17 × self-care ability (self-care vs. complete dependence) −2.79 × self- care ability (partial dependence vs. complete dependence).In the internal validation, the AUC was 0.897 (95% CI = 0.87–0.92; P  < 0.001), with a sensitivity of 0.75 and specificity of 0.93, indicating a high level of discrimination for the model. The Hosmer–Lemeshow test yielded a chi-square (χ 2 ) value of 2.823 with 8 degrees of freedom and a P -value of 0.945, indicating high accuracy of the model. In the external validation, the AUC was 0.839 (95% CI = 0.75–0.93; P  < 0.001), with a sensitivity of 0.73 and specificity of 0.96. The Hosmer–Lemeshow test yielded a χ 2 value of 12.85 with 8 degrees of freedom and a P -value of 0.117. The DCA plot shows that the DCA curve is consistently higher than the two extreme curves, indicating a good fit of the model. Conclusion The predictive model for the risk of unplanned extubation of thoracoabdominal drainage tubes in postoperative patients demonstrates good discrimination and Calibration. It can provide reference for clinical nursing staff in predicting the risk and early development of personalized preventive strategies for drainage tube UEX.

Background Circular RNAs (circRNAs), a novel class of single-stranded, covalently closed non-coding RNA molecules, have been increasingly recognized for their roles in the cellular landscape over the past decade. These molecules are predominantly localized within the cytoplasm of eukaryotic cells and have been implicated in the pathogenesis of a spectrum of cancers. Despite this, the specific contributions of circRNAs to hepatocellular carcinoma (HCC) have yet to be fully delineated. Methods Our research has identified that cyclic RNA0002898 is downregulated in HCC, with its reduced expression correlating with advanced tumor grade and diminished patient survival outcomes. Furthermore, we have demonstrated that the transcription factor forkhead box C2 (FOXC2) regulates the expression of cyclic RNA0002898, and diminished levels of cyclic RNA0002898 in HCC are associated with enhanced tumor cell proliferation, migration, and invasion in vitro. Mechanistic insights revealed that cyclic RNA0002898 could modulate the expression levels of the tumor suppressor a disintegrin and metalloproteinase with thrombospondin motif 5 (ADAMTS5) by antagonizing miR-145, thereby inhibiting angiogenesis and suppressing the growth of HCC cells. Results Our findings collectively elucidate the regulatory role, functional significance, and underlying mechanism of cyclic RNA0002898 in HCC, a previously uncharted relationship. The potential prognostic implications of cyclic RNA0002898 and its therapeutic potential as a target in HCC warrant further investigation.