Clinic Practice Research Articles

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

To develop a predictive model for high-burnout of nurses. A cross-sectional study. 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. 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. This study has developed and validated a predictive nomogram for identifying high-burnout in nurses. 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. The study adhered to the relevant EQUATOR reporting guidelines: TRIPOD Checklist for Prediction Model Development and Validation. No patient or public contribution.

Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning

Progress has been made in chemotherapy drugs, but drug resistance remains a major challenge in cancer treatment. In clinical practice, although there are existing technologies to assess chemotherapy resistance, there is still a lack of rapid and convenient method to monitor different degrees of drug resistance in cancer cells. Furthermore, the cell morphological changes during the progression of drug resistance remain to be fully understood. In this study, we employed a flow imaging platform based on digital holography (DH) and machine learning to classify cancer cells with different degrees of paclitaxel sensitivity. We extracted 112 morphological features from four datasets of single-cell quantitative phase images of epithelial ovarian cancer (EOC) cells and performed classification training using five supervised machine learning algorithms. Both support vector machine (SVM) and neural network (NN) achieved over 90% classification accuracy. To understand the morphological features associated with drug resistance properties of cancer cells, we applied the SHapley Additive exPlanations (SHAP), a model interpretation framework, to quantify and rank the feature contributions for both classification models. The results reported here about this label-free and high-accuracy assessment of different degrees of drug resistance in cancer cells opens the route to exploitation for monitoring chemotherapy in clinic practices.

Causal associations between sarcopenia-related traits and intervertebral disc degeneration: a two-sample mendelian randomization analysis.

Sarcopenia (SP) and intervertebral disc degeneration (IVDD) have a higher incidence in the elderly population. Previous studies have indicated a potential association between SP and IVDD. The objective of this study is to elucidate the potential causal relationship between sarcopenia-related traits and IVDD through Two-sample Mendelian randomization (MR) analysis. We utilized a genome-wide association study conducted on the European population to collect aggregated data on sarcopenia and IVDD. Inverse variance weighting was primarily employed, supplemented by MR Egger, weighted median, simple model, and weighted model methods. Additionally, sensitivity analysis was performed to assess the robustness of the findings. Appendicular lean mass is positively associated with "Other intervertebral disc disorders" (OIDD) and "Prolapsed or slipped disc" (POSD) (OIDD: p = 0.002, OR = 1.120; POSD: p < 0.001, OR = 1.003), while grip strength (GS) is positively associated with POSD (left: p = 0.004, OR = 1.008; right: p < 0.001, OR = 1.010). It is worth mentioning that walking pace has significant causal relationship with "Low back pain" (LBP), "Lower back pain or/and sciatica" (LBPOAS), "Sciatica with lumbago" (SWL) and OIDD (LBP: p < 0.001, OR = 0.204; LBPOAS: p < 0.001, OR = 0.278; SWL: p = 0.003, OR = 0.249; OIDD: p < 0.001, OR = 0.256). The present study revealed the causal relationship between SP-related traits and IVDD and recommended to prevent and treat sarcopenia as a means of preventing IVDD in clinic practice.

Economic evaluation alongside a clinical trial of near-to-patient testing for sexually transmitted infections

BackgroundCurrent clinical care for common bacterial STIs (Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG) and Mycoplasma genitalium (MG)) involves empiric antimicrobial therapy when clients are symptomatic, or if asymptomatic, waiting for laboratory testing and recall if indicated. Near-to-patient testing (NPT) can improve pathogen-specific prescribing and reduce unnecessary or inappropriate antibiotic use in treating sexually transmitted infections (STI) by providing same-day delivery of results and treatment. MethodsWe compared the economic cost of NPT to current clinic practice for managing clients with suspected proctitis, non-gonococcal urethritis (NGU), or as an STI contact, from a health provider’s perspective. With a microsimulation of 1000 clients, we calculated the cost per client tested and per STI- and pathogen- detected for each testing strategy. Sensitivity analyses were conducted to assess the robustness of the main outcomes. Costs are reported as Australian dollars (2023). ResultsIn the standard care arm, cost per client tested for proctitis, NGU in men who have sex with men (MSM) and heterosexual men were the highest at $247.96 (95% Prediction Interval (PI): 246.77–249.15), $204.23 (95% PI: 202.70–205.75) and $195.01 (95% PI: 193.81–196.21) respectively. Comparatively, in the NPT arm, it costs $162.36 (95% PI: 161.43–163.28), $158.39 (95% PI: 157.62–159.15) and $149.17 (95% PI: 148.62–149.73), respectively. Using NPT resulted in cost savings of 34.52%, 22.45% and 23.51%, respectively. Among all the testing strategies, substantial difference in cost per client tested between the standard care arm and the NPT arm was observed for contacts of CT or NG, varying from 27.37% to 35.28%. ConclusionWe found that NPT is cost-saving compared with standard clinical care for individuals with STI symptoms and sexual contacts of CT, NG, and MG.

Nomogram for predicting the risk of postoperative delirium in elderly patients undergoing orthopedic surgery

ObjectiveTo retrospectively analyze the risk factors for postoperative delirium (POD) after orthopedic surgery in elderly patients and establish an individualized nomogram to predict the risk of POD.MethodsThe data of 1011 patients who underwent orthopedic surgery from January 2019 to January 2022 were retrospectively analyzed. Univariate and multivariate logistic analyses were used to screen for independent risk factors. Stepwise regression was conducted to screen risk factors to construct a nomogram to predict the risk of POD after orthopedic surgery in elderly individuals, and nomogram validation analyses were performed.ResultsThe logistic regression results showed that age (≥ 75 years old vs. < 75 years old; odds ratio (OR) = 2.889; 95% confidence interval (CI), 1.149, 7.264), sex (male vs. female, OR = 2.368; 95% CI, 1.066, 5.261), and preoperative cognitive impairment (yes vs. no, OR = 13.587; 95% CI, 4.360, 42.338) were independent risk factors for POD in elderly patients who underwent orthopedic surgery (P < 0.05). A nomogram was constructed using 7 risk factors, i.e., age, American Society of Anesthesiologists (ASA) classification, sex, preoperative hemoglobin (Hb), preoperative pulmonary disease, cognitive impairment, and intraoperative infusion volume. The area under the curve (AUC) showed good discrimination (0.867), the slope of the calibration curve was 1.0, and the optimal net benefit of the nomogram from the decision curve analysis (DCA) was 0.01–0.58.ConclusionThis study used 7 risk factors to construct a nomogram to predict the risk of POD after major orthopedic surgery in elderly individuals, and the nomogram had good discrimination ability, accuracy, and clinical practicability.

A novel nomogram based on inflammation biomarkers for predicting radiation cystitis in patients with local advanced cervical cancer.

Platelet-to-albumin ratio (PAR) is a new systemic inflammatory prognostic indicator associated with many inflammatory diseases. However, its role in radiation cystitis (RC) is obscure. This study aimed to explore whether PAR could be used as an effective parameter for predicting the RC risk in local advanced cervical cancer (CC) treated with radiotherapy. A total of 319 local advanced CC patients who received radical radiotherapy at Fujian Cancer Hospital were enrolled between December 2018 and January 2021. Demographics and clinical parameters were retrospectively analyzed. Univariate and multivariate analyses were used to identify the risk factors for RC. Backward and stepwise regression was applied to construct two monograms-one with primary significant factors and the other with extra inflammatory biomarkers. A DeLong test was applied to compare the prediction abilities of two nomograms. Calibration curves and decision curve analysis (DCA) evaluated its prediction consistency, discrimination ability, and clinical net benefit. Univariate analysis showed that age, tumor size, stage, total radiation dose, pelvic radiation dose, Systemic Immune-Inflammation Index (SII), platelet-to-lymphocyte ratio (PLR), and PAR were significantly associated with RC occurrence (all p < 0.05). Multivariate analyses indicated that age, tumor size, stage, total radiation dose, and PAR were independent factors (all p < 0.05). Then, the area under curve (AUC) value of the nomogramSII+PAR was higher (AUC = 0.774) compared to that of the baseline nomogram (AUC = 0.726) (pDelong = 0.02). Also, the five-cross validation confirmed the stability of the nomogramSII+PAR. Moreover, the calibration curve and DCA exhibited the nomograms' good prediction consistency and clinical practicability. PAR and SII could be valued for CC patients who are treated with radiation therapy. The nomogram based on PAR and SII could stratify patients who need extra intervention and nursing care to prevent bladder radiation damage and improve patients' quality of life.

The Scope of Practice of an Outpatient Pediatric Palliative Care Oncology Clinic

The Scope of Practice of an Outpatient Pediatric Palliative Care Oncology Clinic

Multiplex Digital PCR-Based Development and discussion of the Detection of Genetic Association Between Staphylococcus aureus and mecA.

Methicillin-resistant Staphylococcus aureus (MRSA) is a predominant nosocomial infection-causing bacteria. The aim of this study was to develop a novel single-bacteria multiplex digital PCR assays (SMD-PCR), which is capable of simultaneously detecting and discriminating Methicillin-sensitive Staphylococcus aureus (MSSA) and MRSA. This protocol employed TaqMan probes to detect SAOUHSC_00106 and mecA genes, with the latter being linked to methicillin resistance. A total of 72 samples from various specimen types were evaluated. The accuracy rates for the sputum samples, pus samples, swab samples, ear secretion samples, and catheter samples were 94.44%, 100%, 92%, 100%, and 100%, respectively. Our results showed that the clinical practicability of SMD-PCR has applicability to the rapid detection of MRSA without DNA extraction or bacterial culture, and can be utilized for the rapid detection of Staphylococcus aureus and the timely identification of MRSA in clinical samples, thereby providing an advanced platform for the rapid diagnosis of clinical MRSA infection.

Machine learning models for prediction of postoperative venous thromboembolism in gynecological malignant tumor patients.

To identify risk factors that associated with the occurrence of venous thromboembolism (VTE) within 30 days after hysterectomy among gynecological malignant tumor patients, and to explore the value of machine learning (ML) models in VTE occurrence prediction. A total of 1087 patients between January 2019 and January 2022 with gynecological malignant tumors were included in this single-center retrospective study and were randomly divided into the training dataset (n = 870) and the test dataset (n = 217). Univariate logistic regression analysis was used to identify risk factors that associated with the occurrence of postoperative VTE in the training dataset. Machine learning models (including decision tree (DT) model and logistic regression (LR) model) to predict the occurrence of postoperative VTE were constructed and internally validated. The incidence of developing 30-day postoperative VTE was 6.0% (65/1087). Age, previous VTE, length of stay (LOS), tumor stage, operative time, surgical approach, lymphadenectomy (LND), intraoperative blood transfusion and gynecologic Caprini (G-Caprini) score were identified as risk factors for developing postoperative VTE in gynecological malignant tumor patients (p < 0.05). The AUCs of LR model and DT model for predicting VTE were 0.722 and 0.950, respectively. The ML models, especially the DT model, constructed in our study had excellent prediction value and shed light upon its further application in clinic practice.

Prolidase Enzyme Activity in Endometrial Polyps and Its Relationship with Oxidative Stress

Endometrial polyp (EP) is a condition that is often encountered in obstetrics and gynecology clinic practices. In our study, we aimed to reveal the relationship between prolidase enzyme activity (PEA) and Oxidative Stress (OS) in patients with endometrial polyps. Thirty-five patients who were histopathologically diagnosed with EP and 35 patients with abnormal uterine bleeding (AUB) without pathology as a result of endometrial sampling were included in the control group. Serum TOS, OSI, tissue PEA, and endometrial thickness values were found to be statistically significantly higher in the EP group compared to the control group. We believe that our study, when supported by studies with larger patient groups, may be a useful marker for a more detailed understanding of the pathogenesis and clinical follow-up of patients.

Hepatic Pecoma versus Hepatocellular Carcinoma In The Noncirrhotic Liver on Gd-EOB-DTPA-Enhanced MRI: A Diagnostic Challenge.

Hepatic perivascular epithelioid cell tumors (PEComa) often mimic hepatocellular carcinoma (HCC) in patients without cirrhosis. This study aimed to develop a nomogram using imaging characteristics on Gd-EOB-DTPA-enhanced MRI and to distinguish PEComa from HCC in a noncirrhotic liver. Forty patients with non-cirrhotic Gd-EOB-DTPA-enhanced magnetic resonance imaging(MRI) were included in our study. A multivariate logistic regression model was used to select significant variables to distinguish PEComa from HCC. A nomogram was developed based on the regression model. The performance of the nomogram was assessed with respect to the ROC curve and calibration curve. Decision curve analysis (DCA) was performed to evaluate the clinical usefulness of the nomogram. Two significant predictors were identified: the appearance of an early draining vein and the T1D value of tumors. The ROC curve showed that the area under the curve (AUC) of the model to predict the risk of PEComa was 0.91 (95% CI: 0.80~1) and showed that the model had high specificity (92.3%) and sensitivity (88.9%). The nomogram incorporating these two predictors showed favorable calibration, which was validated using 1000 resampling procedures, and the corrected C-index of this model was 0.90. Furthermore, DCA analysis showed that the model had clinical practicability. In conclusion, the nomogram model showed favorable predictive accuracy for distinguishing PEComa from HCC in non-cirrhotic patients and may aid in clinical decision-making.

A web-based dynamic predictive model for postoperative nausea and vomiting in patient receiving gynecological laparoscopic surgery.

The aim of this study was to develop a web-based dynamic prediction model for postoperative nausea and vomiting (PONV) in patients undergoing gynecologic laparoscopic surgery. The patients (N = 647) undergoing gynecologic laparoscopic surgery were included in this observational study. The candidate risk-factors related to PONV were included through literature search. Lasso regression was utilized to screen candidate risk-factors, and the variables with statistical significance were selected in multivariable logistic model building. The web-based dynamic Nomogram was used for model exhibition. Accuracy and validity of the experimental model (EM) were evaluated by generating receiver operating characteristic (ROC) curves and calibration curves. Hosmer-Lemeshow test was used to evaluate the goodness of fit of the model. Decision curve analysis (DCA) was used to evaluate the clinical practicability of the risk prediction model. Ultimately, a total of five predictors including patient-controlled analgesia (odds ratio [OR], 4.78; 95% confidence interval [CI], 1.98-12.44), motion sickness (OR, 4.80; 95% CI, 2.71-8.65), variation of blood pressure (OR, 4.30; 95% CI, 2.41-7.91), pregnancy vomiting history (OR, 2.21; 95% CI, 1.44-3.43), and pain response (OR, 1.64; 95% CI, 1.48-1.83) were selected in model building. Assessment of the model indicates the discriminating power of EM was adequate (ROC-areas under the curve, 93.0%; 95% CI, 90.7%-95.3%). EM showed better accuracy and goodness of fit based on the results of the calibration curve. The DCA curve of EM showed favorable clinical benefits. This dynamic prediction model can determine the PONV risk in patients undergoing gynecologic laparoscopic surgery.

Development and validation of a nomogram for predicting sever cancer-related fatigue in patients with cervical cancer

ObjectiveCancer-related fatigue (CRF) has been considered the biggest influencing factor for cancer patients after surgery. This study aimed to develop and validate a nomogram for severe cancer-related fatigue (CRF) patients with cervical cancer (CC).MethodsA cross-sectional study was conducted to develop and validate a nomogram (building set = 196; validation set = 88) in the Department of Obstetrics and Gynecology of a Class III hospital in Shenyang, Liaoning Province. We adopted the questionnaire method, including the Cancer Fatigue Scale (CFS), Medical Uncertainty in Illness Scale (MUIS), Medical Coping Modes Questionnaire (MCMQ), Multidimensional Scale of Perceived Social Support (MSPSS), and Sense of Coherence-13 (SOC-13). Binary logistic regression was used to test the risk factors of CRF. The R4.1.2 software was used to develop and validate the nomogram, including Bootstrap resampling method, the ability of Area Under Curve (AUC), Concordance Index (C-Index), Hosmer Lemeshow goodness of fit test, Receiver Operating Characteristic (ROC) curve, Calibration calibration curve, and Decision Curve Analysis curve (DCA).ResultsThe regression equation was Logit(P) = 1.276–0.947 Monthly income + 0.989 Long-term passive smoking − 0.952 Physical exercise + 1.512 Diagnosis type + 1.040 Coping style − 0.726 Perceived Social Support − 2.350 Sense of Coherence. The C-Index of the nomogram was 0.921 (95% CI: 0.877\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\sim $$\\end{document}0.958). The ROC curve showed the sensitivity of the nomogram was 0.821, the specificity was 0.900, and the accuracy was 0.857. AUC was 0.916 (95% CI: 0.876\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\sim $$\\end{document}0.957). The calibration showed that the predicted probability of the nomogram fitted well with the actual probability. The DCA curve showed when the prediction probability was greater than about 10%, the benefit of the nomogram was positive. The results in the validation group were similar.ConclusionThis nomogram had good identifiability, accuracy and clinical practicality, and could be used as a prediction and evaluation tool for severe cases of clinical patients with CC.

Nomogram establishment for short-term survival prediction in ICU patients with aplastic anemia based on the MIMIC-IV database

ABSTRACT Objective To establish an efficient nomogram model to predict short-term survival in ICU patients with aplastic anemia (AA). Methods The data of AA patients in the MIMIC-IV database were obtained and randomly assigned to the training set and testing set in a ratio of 7:3. Independent prognosis factors were identified through univariate and multivariate Cox regression analyses. The variance inflation factor was calculated to detect the correlation between variables. A nomogram model was built based on independent prognostic factors and risk scores for factors were generated. Model performance was tested using C-index, receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and Kaplan-Meier curve. Results A total of 1,963 AA patients were included. A nomogram model with 7 variables was built, including SAPS II, chronic pulmonary obstructive disease, body temperature, red cell distribution width, saturation of peripheral oxygen, age and mechanical ventilation. The C-indexes in the training set and testing set were 0.642 and 0.643 respectively, indicating certain accuracy of the model. ROC curve showed favorable classification performance of nomogram. The calibration curve reflected that its probabilistic prediction was reliable. DCA revealed good clinical practicability of the model. Moreover, the Kaplan-Meier curve showed that receiving mechanical ventilation could improve the survival status of AA patients in the short term but did not in the later period. Conclusion The nomogram model of the short-term survival rate of AA patients was built based on clinical characteristics, and early mechanical ventilation could help improve the short-term survival rate of patients.

Acute Pancreatitis in Pregnancy: A Propensity Score Matching Analysis and Dynamic Nomogram for Risk Assessment.

Acute pancreatitis is easily confused with abdominal pain symptoms, and it could lead to serious complications for pregnant women and fetus, the mortality was as high as 3.3% and 11.6-18.7%, respectively. However, there is still lack of sensitive laboratory markers for early diagnosis of APIP and authoritative guidelines to guide treatment. The purpose of this study was to explore the risk factors of acute pancreatitis in pregnancy, establish, and evaluate the dynamic prediction model of risk factors in acute pancreatitis in pregnancy patients. Clinical data of APIP patients and non-pregnant acute pancreases patients who underwent regular antenatal check-ups during the same period were collected. The dataset after propensity matching was randomly divided into training set and verification set at a ratio of 7:3. The model was constructed using Logistic regression, least absolute shrinkage and selection operator regression, R language and other methods. The training set model was used to construct the diagnostic nomogram model and the validation set was used to validate the model. Finally, the accuracy and clinical practicability of the model were evaluated. A total of 111 APIP were included. In all APIP patients, hyperlipidemic pancreatitis was the most important reason. The levels of serum amylase, creatinine, albumin, triglyceride, high-density lipoprotein cholesterol, and apolipoprotein A1 were significantly different between the two groups. The propensity matching method was used to match pregnant pancreatitis patients and pregnant non-pancreatic patients 1:1 according to age and gestational age, and the matching tolerance was 0.02. The multivariate logistic regression analysis of training set showed that diabetes, triglyceride, Body Mass Index, white blood cell, and C-reactive protein were identified and entered the dynamic nomogram. The area under the ROC curve of the training set was 0.942 and in validation set was 0.842. The calibration curve showed good predictive in training set, and the calibration performance in the validation set was acceptable. The calibration curve showed the consistency between the nomogram model and the actual probability. The dynamic nomogram model we constructed to predict the risk factors of acute pancreatitis in pregnancy has high accuracy, discrimination, and clinical practicability.

A multi-omics study to monitor senescence-associated secretory phenotypes of Alzheimer's disease.

Alzheimer's disease (AD) is characterized by the progressive degeneration and damage of neurons in the brain. However, developing an accurate diagnostic assay using blood samples remains a challenge in clinic practice. The aim of this study was to explore senescence-associated secretory phenotypes (SASPs) in peripheral blood using mass spectrometry based multi-omics approach and to establish diagnostic assays for AD. This retrospective study included 88 participants, consisting of 29 AD patients and 59 cognitively normal (CN) individuals. Plasma and serum samples were examined using high-resolution mass spectrometry to identify proteomic and metabolomic profiles. Receiver operating characteristic (ROC) analysis was employed to screen biomarkers with diagnostic potential. K-nearest neighbors (KNN) algorithm was utilized to construct a multi-dimensional model for distinguishing AD from CN. Proteomics analysis revealed upregulation of five plasma proteins in AD, including RNA helicase aquarius (AQR), zinc finger protein 587B (ZNF587B), C-reactive protein (CRP), fibronectin (FN1), and serum amyloid A-1 protein (SAA1), indicating their potential for AD classification. Interestingly, KNN-based three-dimensional model, comprising AQR, ZNF587B, and CRP, demonstrated its high accuracy in AD recognition, with evaluation possibilities of 0.941, 1.000, and 1.000 for the training, testing, and validation datasets, respectively. Besides, metabolomics analysis suggested elevated levels of serum phenylacetylglutamine (PAGIn) in AD. The multi-omics outcomes highlighted the significance of the SASPs, specifically AQR, ZNF587B, CRP, and PAGIn, in terms of their potential for diagnosing AD and suggested neuronal aging-associated pathophysiology.

Rapid and sensitive visual detection of avian leukosis virus by reverse transcription loop-mediated isothermal amplification combined with a lateral flow immunochromatographic strip assay.

Considering that avian leukosis virus (ALV) infection has inflicted massive economic losses on the poultry breeding industry in most countries, its early diagnosis remains an important measure for timely treatment and control of the disease, for which a rapid and sensitive point-of-care test is required. We established a user-friendly, economical, and rapid visualization method for ALV amplification products based on reverse transcription loop-mediated isothermal amplification (RT-LAMP) combined with an immunochromatographic strip in a lateral flow device (LFD). Using the ALVp27 gene as the target, five RT-LAMP primers and one fluorescein-isothiocyanate-labeled probe were designed. After 60 min of RT-LAMP amplification at 64 °C, the products could be visualized directly using the LFD. The detection limit of this assay for ALV detection was 102 RNA copies/μL, and the sensitivity was 100 times that of reverse transcription polymerase chain reaction (RT-PCR), showing high specificity and sensitivity. To verify the clinical practicality of this assay for detecting ALV, the gold standard RT-PCR method was used for comparison, and consistent results were obtained with both assays. Thus, the assay described here can be used for rapid detection of ALV in resource-limited environments.

Use of a digital tool to support the diagnostic process in memory clinics–a usability study

BackgroundBoth memory clinic professionals and patients see value in digital tools, yet these hardly find their way to clinical practice. We explored the usability of a digital tool to support the diagnostic work-up in daily memory clinic practice. We evaluated four modules that integrate multi-modal patient data (1.cognitive test; cCOG, and 2. MRI quantification; cMRI) into useful diagnostic information for clinicians (3. cDSI) and understandable and personalized information for patients (4. patient report).MethodsWe conducted a mixed-methods study in five Dutch memory clinics. Fourteen clinicians (11 geriatric specialists/residents, two neurologists, one nurse practitioner) were invited to integrate the tool into routine care with 43 new memory clinic patients. We evaluated usability and user experiences through quantitative data from questionnaires (patients, care partners, clinicians), enriched with thematically analyzed qualitative data from interviews (clinicians).ResultsWe observed wide variation in tool use among clinicians. Our core findings were that clinicians: 1) were mainly positive about the patient report, since it contributes to patient-centered and personalized communication. This was endorsed by patients and care partners, who indicated that the patient report was useful and understandable and helped them to better understand their diagnosis, 2) considered the tool acceptable in addition to their own clinical competence, 3) indicated that the usefulness of the tool depended on the patient population and purpose of the diagnostic process, 4) addressed facilitators (ease of use, practice makes perfect) and barriers (high workload, lack of experience, data unavailability).ConclusionThis multicenter usability study revealed a willingness to adopt a digital tool to support the diagnostic process in memory clinics. Clinicians, patients, and care partners appreciated the personalized diagnostic report. More attention to education and training of clinicians is needed to utilize the full functionality of the tool and foster implementation in actual daily practice. These findings provide an important step towards a lasting adoption of digital tools in memory clinic practice.

A novel method of assessing intraoperative surgical margins in patients with dermatofibrosarcoma protuberans: A specimen radiography system.

Background Dermatofibrosarcoma protuberans (DFSP) is one of the most challenging cutaneous cancers in surgical clinic practice. Excision with negative margins is essential for effective disease control. However, wide surgical margins and maximal tissue conservation are mutually exclusive. Mohs micrographic surgery conserves tissue but is time-consuming. Thus, we developed a novel specimen radiography system that can be used intraoperatively. Aims To introduce a specimen radiography system for evaluating intraoperative surgical margins in patients with dermatofibrosarcoma protuberans. Methods Since September 2017, we have treated seven biopsy-proven cases of local DFSPs via local excision with surgical margins of 2-4 cm. During operations, the operative specimens were screened using the specimen radiography system. All surgical specimens were pathologically examined intraoperatively. Results Five patients were men and two were women, of median age 36 years. The mean radiographic screening time was 9.7 ± 2.3 min. Radiographically negative margins were confirmed intraoperatively. The minimal margin width ranged from 5.0 to 35.4 mm (mean width 16.9 ± 10.4 mm). The intraoperatively negative radiographic margins were consistent with those revealed by postoperative pathology. The minimal pathological margin width ranged from 4.0 to 34.5 mm (mean 16.6 ± 10.1 mm) and was not significantly different from the intraoperative data. Limitations The sample size was small and positive or negative predictive values were not calculated. Conclusions We introduce a novel method of intraoperative surgical margin assessment for DFSP patients. It may find broad clinical and research applications during oncoplastic surgery.

PELATIHAN MANAJEMEN REKAM MEDIS DAN INFORMASI KESEHATAN PETUGAS REKAM MEDIS SEBAGAI CLINICAL INSTRUCTUR (CI) DI PUSKESMAS KAB.CIREBON

Education and training is one of the efforts to increase human resources and a series of activities or efforts that are carried out continuously, gradually and integratedly. The training was carried out to develop Clinical Instructor competency in organizing student clinical practice activities at community health centers. The limited knowledge of Clinical Instructors in terms of competencies that must be achieved by practical students influences the implementation of clinical practical learning and when giving assessments. One of the competencies that must be achieved during clinical practice is the basic management of medical records. This research aims to increase CI's knowledge and competence in implementing clinical practice guidance for students. The method used is training with several stages of activities, namely competency identification analysis in the form of training system design, then implementation of activities, and evaluation. The increase in competency is proven by an increase in post test results from an average pre test score of 71.59 and an average post test result of 87.73. The evaluation results of training implementation can be assessed on 4 scales, namely training implementation, speakers, materials, etc. Each scale has an average value of 3.5, these results prove that the training implementation process is in the good category. Implementation of training can improve the competency of medical record officers as Clinical Instructors in administering clinical practice students at community health centers.