Longitudinal Prediction of Freezing of Gait in Parkinson's Disease: A Prospective Cohort Study

Objective: Freezing of gait (FOG) is a disabling complication in Parkinson's disease (PD). Yet, studies on a validated model for the onset of FOG based on longitudinal observation are absent. This study aims to develop a risk prediction model to predict the probability of future onset of FOG from a multicenter cohort of Chinese patients with PD.Methods: A total of 350 patients with PD without FOG were prospectively monitored for ~2 years. Demographic and clinical data were investigated. The multivariable logistic regression analysis was conducted to develop a risk prediction model for FOG.Results: Overall, FOG was observed in 132 patients (37.70%) during the study period. At baseline, longer disease duration [odds ratio (OR) = 1.214, p = 0.008], higher total levodopa equivalent daily dose (LEDD) (OR = 1.440, p < 0.001), and higher severity of depressive symptoms (OR = 1.907, p = 0.028) were the strongest predictors of future onset of FOG in the final multivariable model. The model performed well in the development dataset (with a C-statistic = 0.820, 95% CI: 0.771–0.865), showed acceptable discrimination and calibration in internal validation, and remained stable in 5-fold cross-validation.Conclusion: A new prediction model that quantifies the risk of future onset of FOG has been developed. It is based on clinical variables that are readily available in clinical practice and could serve as a small tool for risk counseling.

Establishment and Evaluation of a Risk-prediction Model for HBP in Elderly Patients With NAFLD From a Health Management Perspective

Objective: Elderly patients with nonalcoholic fatty liver disease (NAFLD) are at a higher risk of developing high blood pressure (HBP) and having a low quality of life. This study established an effective, individualised, early HBP risk-prediction model and proposed health management advice for the ³60 patients with NAFLD in Shanghai, China.Methods: Questionnaire surveys, physical examinations, and biochemical tests were conducted on 7,319 cases of sample data. Risk factors were screened using the least absolute shrinkage and selection operator (Lasso) model and random forest (RF) model. A risk-prediction model was established using logistic regression analysis and dynamic nomogram was drawn. The model was evaluated for discrimination, calibration, and clinical applicability using receiver operating characteristic curves (ROC), calibration curves, decision curve analysis (DCA), net reclassification index (NRI), and external validation.Results: The results suggested the model showed moderate predictive ability. The area under curve (AUC) of internal validation was 0.707 (95% CI: 0.688-0.727), the external validation AUC was 0.688 (95% CI: 0.672-0.705). The calibration plots showed good calibration, the risk threshold of the decision curve was 30-56%, and the NRI value was 0.109.Conclusion: This HBP risk factor model may be used in clinical practice to predict the HBP risk in NAFLD patients.

Infection Risk Prediction Model for COVID-19 Based on an Analysis of the Settlement of Particles Generated during Dental Procedures in Dental Clinics

Background. The health emergency declaration owing to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has drawn attention toward nosocomial transmission. The transmission of the disease varies depending on the environmental conditions. Saliva is a recognized SARS-CoV-2 reservoir in infected individuals. Therefore, exposure to fluids during dental procedures leads to a high risk of contagion. Objective. This study aimed to develop an infection risk prediction model for COVID-19 based on an analysis of the settlement of the aerosolized particles generated during dental procedures. Materials and Methods. The settlement of aerosolized particles during dental aerosol-generating procedures (AGPs) performed on phantoms was evaluated using colored saliva. The gravity-deposited particles were registered using a filter paper within the perimeter of the phantom head, and the settled particles were recorded in standardized photographs. Digital images were processed to analyze the stained area. A logistic regression model was built with the variables ventilation, distance from the mouth, instrument used, area of the mouth treated, and location within the perimeter area. Results. The largest percentage of the areas stained by settled particles ranged from 1 to 5 µm. The maximum settlement range from the mouth of the phantom head was 320 cm, with a high-risk cutoff distance of 78 cm. Ventilation, distance, instrument used, area of the mouth being treated, and location within the perimeter showed association with the amount of settled particles. These variables were used for constructing a scale to determine the risk of exposure to settled particles in dentistry within an infection risk prediction model. Conclusion. The greatest risk of particle settlement occurs at a distance up to 78 cm from the phantom mouth, with inadequate ventilation, and when working with a high-speed handpiece. The majority of the settled particles generated during the AGPs presented stained areas ranging from 1 to 5 µm. This model was useful for predicting the risk of exposure to COVID-19 in dental practice.

Personalized 5-Year Prostate Cancer Risk Prediction Model in Korea Based on Nationwide Representative Data

Prostate cancer is the fourth most common cause of cancer in men in Korea, and there has been a rapid increase in cases. In the present study, we constructed a risk prediction model for prostate cancer using representative data from Korea. Participants who completed health examinations in 2009, based on the Korean National Health Insurance database, were eligible for the present study. The crude and adjusted risks were explored with backward selection using the Cox proportional hazards model to identify possible risk variables. Risk scores were assigned based on the adjusted hazard ratios, and the standardized points for each risk factor were proportional to the β-coefficient. Model discrimination was assessed using the concordance statistic (c-statistic), and calibration ability was assessed by plotting the mean predicted probability against the mean observed probability of prostate cancer. Among the candidate predictors, age, smoking intensity, body mass index, regular exercise, presence of type 2 diabetes mellitus, and hypertension were included. Our risk prediction model showed good discrimination (c-statistic: 0.826, 95% confidence interval: 0.821–0.832). The relationship between model predictions and actual prostate cancer development showed good correlation in the calibration plot. Our prediction model for individualized prostate cancer risk in Korean men showed good performance. Using easily accessible and modifiable risk factors, this model can help individuals make decisions regarding prostate cancer screening.

Assessment of performance of the machine learning-based breast cancer risk prediction model: a systematic review and meta-analysis (Preprint)

BACKGROUND Background: Machine learning algorithms well-suited in cancer research, especially in breast cancer for the investigation and development of riTo assess the performance of available machine learning-based breast cancer risk prediction model. OBJECTIVE Objective: To assess the performance of available machine learning-based breast cancer risk prediction model. METHODS Methods: As of June 9, 2021, articles on breast cancer risk prediction models by machine learning were searched in PubMed, Embase, and Web of Science. Studies describing the development or validation of risk prediction models for predicting future breast cancer risk were included. Pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model. RESULTS Result: A total of 8 studies with 10 datasets were included. Neural network was the most common machine learning method for the development of risk prediction models. The pooled AUC of machine learning-based optimal risk prediction model reported in each study was 0.73 (95%CI: 0.66-0.80), which was higher than that of traditional risk factor-based risk prediction models (all Pheterogeneity < 0.001). The pooled AUC of neural network-based risk prediction model was higher than that of non-neural network-based optimal risk prediction model (0.71 vs. 0.68). Subgroup analysis showed that incorporation of imaging features risk models had a higher pooled AUC than model of non-incorporation of imaging features (0.73 vs. 0.61; Pheterogeneity =0.001). CONCLUSIONS Conclusions: The pooled machine learning-based breast cancer risk prediction model yield a good prediction performance and promising results.

Development and Validation of a Risk Prediction Model for Patients with COVID-19 Using Linked Population Health Data (Preprint)

BACKGROUND The impact of the COVID-19 pandemic on health care utilisation and associated costs has been significant, with one in ten patients becoming severely ill and being admitted to hospital with serious complications during the first wave of the pandemic. Risk prediction models can help health care providers identify high-risk patients in their populations and intervene to improve health outcomes and reduce associated costs. OBJECTIVE To develop and validate a hospitalisation risk prediction model for adult patients with laboratory confirmed Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). METHODS The model was developed using pre-linked and standardised data of adult patients with laboratory confirmed SARS-CoV-2 from Cerner’s population health management platform (HealtheIntent®) in the London Borough of Lewisham. A total of 14,203 patients who tested positive for SARS-CoV-2 between 1st March 2020 and 28th February 2021 were included in the development and internal validation cohorts. A second temporal validation cohort covered the period between 1st March 2021 to 30th April 2021. The outcome variable was hospital admission in adult patients with laboratory confirmed SARS-CoV-2. A generalised linear model was used to train the model. The predictive performance of the model was assessed using the area under the receiver operator characteristic curve (ROC-AUC). RESULTS Overall, 14,203 patients were included. Of those, 9,755 (68.7%) were assigned to the development cohort, 2,438 (17.2%) to the internal validation cohort, and 2,010 (14.1%) to the temporal validation cohort. A total of 917 (9.4%) patients were admitted to hospital in the development cohort, 210 (8.6%) in the internal validation cohort, and a further 204 (10.1%) in the temporal validation cohort. The model had a ROC-AUC of 0.85 in both the development and validation cohorts. The most predictive factors were older age, male sex, Asian or Other ethnic minority background, obesity, chronic kidney disease, hypertension and diabetes. CONCLUSIONS The COVID-19 hospitalisation risk prediction model demonstrated very good performance and can be used to stratify risk in the Lewisham population to help providers reduce unnecessary hospital admissions and associated costs, improve patient outcomes, and target those at greatest risk to ensure full vaccination against SARS-CoV-2. Further research may examine the external validity of the model in other populations.

A Nomogram for Predicting In-Hospital Mortality in Patients with Takotsubo Syndrome

BACKGROUND Despite the large number of studies focus on the prognosis and in-hospital outcomes risk factors of patients with takotsubo syndrome, there was still lack of utility and visual risk prediction model for predicting the in-hospital mortality of patients with takotsubo syndrome. OBJECTIVES Our study aimed to establish a utility risk prediction model for the prognosis of in-hospital patients with takotsubo syndrome (TTS). METHODS The study is a retrospective cohort study. Model of in-hospital mortality of TTS patients was developed by multivariable logistic regression analysis. Calibration and discrimination were used to assess the performance of the nomogram. The clinical utility of the model was evaluated by decision curve analysis (DCA). RESULTS Overall, 368 TTS patients (320 Survivals and 48 deaths) were included in our research from MIMIC-IV database. The incidence of in-hospital mortality with TTS is 13.04%. Lasso regression and multivariate logistic regression model verified that potassium, pt, age, myocardial infarction, WBC, hematocrit, anion gap and SOFA score were significantly associated with in-hospital mortality of TTS patients. The nomogram demonstrated a good discrimination with a AUC of ROC 0.811(95%Cl: 0.746-0.876) in training set and 0.793(95%Cl: 0.724-0.862) in test set. The calibration plot of risk prediction model showed predicted probabilities against observed death rates indicated excellent concordance. DCA showed that the nomogram has good clinical benefits. Conclusion We developed a nomogram that predict hospital mortality in patients with TTS according to clinical data. The nomogram exhibited excellent discrimination and calibration capacity, favoring its clinical utility.