OBJECTIVE: Patients with heart valvular regurgitation is increasing; early screening of potential patients developing heart failure (HF) is crucial.
METHODS: From 1 November 2019 to 31 October 2023, a total of 509 patients with heart valvular regurgitation hospitalized in the Department of Cardiovascular Disease of the First Affiliated Hospital of Guangzhou University of Traditional Medicine were enrolled. Three hundred fifty-six cases were selected as the training set for modelling, and 153 cases were selected as the validation set for the internal validation of the model.
RESULTS: A predictive model of heart failure with the following nine risk factors was developed: atrial fibrillation (AF), pulmonary infection (PI), coronary artery disease (CAD), creatinine (CREA), low-density lipoprotein cholesterol (LDL-C), d-dimer (DDi), left ventricular end-diastolic diameter (LVEDd), mitral regurgitation (MR) and aortic regurgitation (AR). The model was evaluated by the C-index [the training set: area under curve (AUC) 0.937, 95% confidence interval (CI) 0.911-0.963; the validation set: AUC 0.928, 95% CI 0.890-0.967]. Hosmer-Lemeshow test (the training set: χ2 10.908, P = 0.207; the validation set: χ2 4.896, P = 0.769) revealed that both the training and validation sets performed well in terms of model differentiation and calibration. Decision curve analysis showed that both the training and validation sets have higher net benefits, indicating that the model has good utility. Ten-fold cross-validation showed that the training set has high similarities with the validation set, which means that the model has good stability.
CONCLUSIONS: The occurrence of heart failure in patients with valvular regurgitation has a significant correlation with AF, PI, CAD, CREA, LDL-C, DDi, LVEDd, MR and AR. Based on these risk factors, a prediction model for heart failure was developed and validated, which showed good differentiation and utility, high accuracy and stability, providing a method for predicting heart failure.

Clinical prediction models provide risks of health outcomes that can inform patients and support medical decisions. However, most models never make it to actual implementation in practice. A commonly heard reason for this lack of implementation is that prediction models are often not externally validated. While we generally encourage external validation, we argue that an external validation is often neither sufficient nor required as an essential step before implementation. As such, any available external validation should not be perceived as a license for model implementation. We clarify this argument by discussing 3 common misconceptions about external validation. We argue that there is not one type of recommended validation design, not always a necessity for external validation, and sometimes a need for multiple external validations. The insights from this paper can help readers to consider, design, interpret, and appreciate external validation studies.

BACKGROUND: People with opioid use disorder have substantially higher standardised mortality rates compared to the general population; however, lack of clear individual prognostic information presents challenges to prioritise or target interventions within drug treatment services. Previous prognostic models have been developed to estimate the risk of developing opioid use disorder and opioid-related overdose in people routinely prescribed opioids but, to our knowledge, none have been developed to estimate mortality risk in people accessing drug services with opioid use disorder. Initial presentation to drug services is a pragmatic time to evaluate mortality risk given the contemporaneous routine collection of prognostic indicators and as a decision point for appropriate service prioritisation and targeted intervention delivery. This study aims to develop and internally validate a model to estimate 6-month mortality risk for people with opioid use disorder from prognostic indicators recorded at initial assessment in drug services in England.
METHODS: An English national dataset containing records from individuals presenting to drug services between 1 April 2013 and 1 April 2023 (n > 800,000) (the National Drug Treatment Monitoring System (NDTMS)) linked to their lifetime hospitalisation and death records (Hospital Episode Statistics-Office of National Statistics (HES-ONS)). Twelve candidate prognostic indicator variables were identified based on literature review of demographic and clinical features associated with increased mortality for people in treatment for opioid use disorder. Variables will be extracted at initial presentation to drug services with mortality measured at 6 months. Two multivariable Cox regression models will be developed one for 6-month all-cause mortality and one for 6-month drug-related mortality using backward elimination with a fractional polynomial approach for continuous variables. Internal validation will be undertaken using bootstrapping methods. Discrimination of both models will be reported using Harrel\'s c and d-statistics. Calibration curves and slopes will be presented comparing expected and observed event rates.
CONCLUSIONS: The models developed and internally validated in this study aim to improve clinical assessment of mortality risk for people with opioid use disorder presenting to drug services in England. External validation in different populations will be required to develop the model into a tool to assist future clinical decision-making.

Background: The coronavirus disease 2019 (COVID-19) pandemic has found the whole world unprepared for its correct management. Italy was the first European country to experience the spread of the SARS-CoV-2 virus at the end of February 2020. As a result of hospital overcrowding, the quality of care delivered was not always optimal. A substantial number of patients admitted to non-ICU units could have been treated at home. It would have been extremely useful to have a score that, based on personal and clinical characteristics and simple blood tests, could have predicted with sufficient reliability the probability that a patient had or did not have a disease that could have led to their death. This study aims to develop a scoring system to identify which patients with COVID-19 are at high mortality risk upon hospital admission, to expedite and enhance clinical decision making. Methods: A retrospective analysis was performed to develop a multivariable prognostic prediction model. Results: Derivation and external validation cohorts were obtained from two Italian University Hospital databases, including 388 (10.31% deceased) and 1357 (7.68% deceased) patients with confirmed COVID-19, respectively. A multivariable logistic model was used to select seven variables associated with in-hospital death (age, baseline oxygen saturation, hemoglobin value, white blood cell count, percentage of neutrophils, platelet count, and creatinine value). Calibration and discrimination were satisfactory with a cumulative AUC for prediction mortality of 0.924 (95% CI: 0.893-0.944) in derivation cohorts and 0.808 (95% CI: 0.886-0.828) in external validation cohorts. The risk score obtained was compared with the ISARIC 4C Mortality Score, and with all the other most important scores considered so far, to evaluate the risk of death of patients with COVID-19. It performed better than all the above scores to evaluate the predictability of dying. Its sensitivity, specificity, and AUC were higher than the other COVID-19 scoring systems when the latter were calculated for the 388 patients in our derivation cohort. Conclusions: In conclusion, the CZ-COVID-19 Score may help all physicians by identifying those COVID-19 patients who require more attention to provide better therapeutic regimens or, on the contrary, by identifying those patients for whom hospitalization is not necessary and who could therefore be sent home without overcrowding healthcare facilities. We developed and validated a new risk score based on seven variables for upon-hospital admission of COVID-19 patients. It is very simple to calculate and performs better than all the other similar scores to evaluate the predictability of dying.

BACKGROUND: Most surveillance systems for catheter-related bloodstream infections (CRBSI) and central line-associated bloodstream infections (CLABSI) are based on manual chart review. Our objective was to validate a fully automated algorithm for CRBSI and CLABSI surveillance in intensive care units (ICU).
METHODS: We developed a fully automated algorithm to detect CRBSI, CLABSI and ICU-onset bloodstream infections (ICU-BSI) in patients admitted to the ICU of a tertiary care hospital in Switzerland. The parameters included in the algorithm were based on a recently performed systematic review. Structured data on demographics, administrative data, central vascular catheter and microbiological results (blood cultures and other clinical cultures) obtained from the hospital\'s data warehouse were processed by the algorithm. Validation for CRBSI was performed by comparing results with prospective manual BSI surveillance data over a 6-year period. CLABSI were retrospectively assessed over a 2-year period.
RESULTS: From January 2016 to December 2021, 854 positive blood cultures were identified in 346 ICU patients. The median age was 61.7 years [IQR 50-70]; 205 (24%) positive samples were collected from female patients. The algorithm detected 5 CRBSI, 109 CLABSI and 280 ICU-BSI. The overall CRBSI and CLABSI incidence rates determined by automated surveillance for the period 2016 to 2021 were 0.18/1000 catheter-days (95% CI 0.06-0.41) and 3.86/1000 catheter days (95% CI: 3.17-4.65). The sensitivity, specificity, positive predictive and negative predictive values of the algorithm for CRBSI, were 83% (95% CI 43.7-96.9), 100% (95% CI 99.5-100), 100% (95% CI 56.5-100), and 99.9% (95% CI 99.2-100), respectively. One CRBSI was misclassified as an ICU-BSI by the algorithm because the same bacterium was identified in the blood culture and in a lower respiratory tract specimen. Manual review of CLABSI from January 2020 to December 2021 (n = 51) did not identify any errors in the algorithm.
CONCLUSIONS: A fully automated algorithm for CRBSI and CLABSI detection in critically-ill patients using only structured data provided valid results. The next step will be to assess the feasibility and external validity of implementing it in several hospitals with different electronic health record systems.

The use of TDM in clinical practice to monitor the plasma levels of antibiotics administered to critically ill patients is a well-established approach that allows for optimization of the patient\'s response to drug therapy, considering the characteristics of the drug, the clinical and physiological status of the patient and any peculiar of the pathogen that caused the clinical picture. In our laboratory, we have developed a single LC-MS/MS analysis for dosing the serum concentration of an antibacterial panel composed of eight antibacterial and two selective inhibitors. The method presented used a certified material furnished by a commercial company and was internally validated using the EMA guidelines. The results have shown high sensitivity, precision, and accuracy, a lower matrix effect combined with simple sample preparation and a time-saving procedure. We have evaluated the recovery rate and matrix effect by testing serum samples without pathological index and serum pools obtained from haemolysed, icteric, or lipemic samples. The assay has shown a recovery range between 94% and 101%.

UNASSIGNED: To investigate the risk factors associated with preeclampsia in hyperglycemic pregnancies and develop a predictive model based on routine pregnancy care.
UNASSIGNED: The retrospective collection of clinical data was performed on 951 pregnant women with hyperglycemia, including those diagnosed with diabetes in pregnancy (DIP) and gestational diabetes mellitus (GDM), who delivered after 34 weeks of gestation at the Maternal and Child Health Hospital Affiliated to Anhui Medical University between January 2017 and December 2019. Observation indicators included liver and kidney function factors testing at 24-29+6 weeks gestation, maternal age, and basal blood pressure. The indicators were screened univariately, and the \"rms\" package in R language was applied to explore the factors associated with PE in HIP pregnancy by stepwise regression. Multivariable logistic regression analysis was used to develop the prediction model. Based on the above results, a nomogram was constructed to predict the risk of PE occurrence in pregnant women with HIP. Then, the model was evaluated from three aspects: discrimination, calibration, and clinical utility. The internal validation was performed using the bootstrap procedure.
UNASSIGNED: Multivariate logistic regression analysis showed that cystatin C, uric acid, glutamyl aminotransferase, blood urea nitrogen, and basal systolic blood pressure as predictors of PE in pregnancy with HIP. The predictive model yielded an area under curve (AUC) value of 0.8031 (95% CI: 0.7383-0.8679), with an optimal threshold of 0.0805, at which point the sensitivity was 0.8307 and specificity of 0.6604. Hosmer-Lemeshow test values were P = 0.3736, Brier score value was 0.0461. After 1000 Bootstrap re-samplings for internal validation, the AUC was 0.7886, the Brier score was 0.0478 and the predicted probability of the calibration curve was similar to the actual probability. A nomogram was constructed based on the above to visualize the model.
UNASSIGNED: This study developed a model for predicting PE in pregnant women with HIP, achieving high predictive performance of PE risk through the information of routine pregnancy care.

BACKGROUND: With an increasing number of web-based calculators designed to provide the probabilities of an individual achieving improvement after lumbar spine surgery, there is a need to determine the accuracy of these models.
OBJECTIVE: To perform an internal and external validation study of the reduced Quality Outcomes Database web-based Calculator (QOD-Calc).
METHODS: Observational longitudinal cohort.
METHODS: Patients enrolled study-wide in Quality Outcomes Database (QOD) and patients enrolled in DaneSpine at a single institution who had elective lumbar spine surgery with baseline data to complete QOD-Calc and 12-month postoperative data.
METHODS: Oswestry Disability Index (ODI), Numeric Rating Scales (NRS) for back and leg pain, EuroQOL-5D (EQ-5D).
METHODS: Baseline data elements were entered into QOD-Calc to determine the probability for each patient having Any Improvement and 30% Improvement in NRS leg pain, back pain, EQ-5D and ODI. These probabilities were compared with the actual 12-month postop data for each of the QOD and DaneSpine cases. Receiver-operating characteristics analyses were performed and calibration plots created to assess model performance.
RESULTS: 24,755 QOD cases and 8,105 DaneSpine lumbar cases were included in the analysis. QOD-Calc had acceptable to outstanding ability (AUC: 0.694-0.874) to predict Any Improvement in the QOD cohort and moderate to acceptable ability (AUC: 0.658-0.747) to predict 30% Improvement. QOD-Calc had acceptable to exceptional ability (AUC: 0.669-0.734) to predict Any improvement and moderate to exceptional ability (AUC: 0.619-0.862) to predict 30% Improvement in the DaneSpine cohort. AUCs for the DaneSpine cohort was consistently lower that the AUCs for the QOD validation cohort.
CONCLUSIONS: QOD-Calc performs well in predicting outcomes in a patient population that is similar to the patients that was used to develop it. Although still acceptable, model performance was slightly worse in a distinct population, despite the fact that the sample was more homogenous. Model performance may also be attributed to the low discrimination threshold, with close to 90% of cases reporting Any Improvement in outcome. Prediction models may need to be developed that are highly specific to the characteristics of the population.

UNASSIGNED: Perineal trauma is a common complication of childbirth and can have serious impacts on long-term health. Few studies have examined the combined effect of multiple risk factors. We developed and internally validated a risk prediction model to predict third and fourth degree perineal tears using data from a general obstetric population.
UNASSIGNED: Risk prediction model using data from all singleton vaginal deliveries at Cork University Maternity Hospital (CUMH), Ireland during 2019 and 2020. Third/fourth degree tears were diagnosed by an obstetrician or midwife at time of birth and defined as tears that extended into the anal sphincter complex or involved both the anal sphincter complex and anorectal mucosa. We used univariable and multivariable logistic regression with backward stepwise selection to develop the models. Candidate predictors included infant sex, maternal age, maternal body mass index, parity, mode of delivery, birthweight, post-term delivery, induction of labour and public/private antenatal care. We used the receiver operating characteristic (ROC) curve C-statistic to assess discrimination, and bootstrapping techniques were used to assess internal validation.
UNASSIGNED: Of 8,403 singleton vaginal deliveries, 8,367 (99.54%) had complete data on predictors for model development. A total of 128 women (1.53%) had a third/fourth degree tear. Three variables remained in the final model: nulliparity, mode of delivery (specifically forceps delivery or ventouse delivery) and increasing birthweight (per 100 gram increase) (C-statistic: 0.75, 95% CI: 0.71, 0.79). We developed a nomogram to calculate individualised risk of third/fourth degree tears using these predictors. Bootstrapping indicated good internal performance.
UNASSIGNED: Use of our nomogram can provide an individualised risk assessment of third/fourth degree tears and potentially aid counselling of women on their potential risk.

The GA118-24B Genetic Analyzer (hereafter, \"GA118-24B\") is an independently developed capillary electrophoresis instrument. In the present research, we designed a series of validation experiments to test its performance at detecting DNA fragments compared to the Applied Biosystems 3500 Genetic Analyzer (hereafter, \"3500\"). Three commercially available autosomal short tandem repeat multiplex kits were used in this validation. The results showed that GA118-24B had acceptable spectral calibration for three kits. The results of accuracy and concordance studies were also satisfactory. GA118-24B showed excellent precision, with a standard deviation of less than 0.1 bp. Sensitivity and mixture studies indicated that GA118-24B could detect low-template DNA and complex mixtures as well as the results generated by 3500 in parallel experiments. Based on the experimental results, we set specific analytical and stochastic thresholds. Besides, GA118-24B showed superiority than 3500 within certain size ranges in the resolution study. Instead of conventional commercial multiplex kits, GA118-24B performed stably on a self-developed eight-dye multiplex system, which were not performed on 3500 Genetic Analyzer. We compared our validation results with those of previous research and found our results to be convincing. Overall, we conclude that GA118-24B is a stable and reliable genetic analyzer for forensic DNA identification.