How to develop a more accurate risk prediction model when there are few events

Ten-Year Survival Rate of 82% in 262 Cases of Arthroscopic Bone Marrow Stimulation for Osteochondral Lesions of the Talus

BACKGROUND

The long-term sustainability of arthroscopic bone marrow stimulation (BMS) for osteochondral lesions of the talus (OLT) remains a matter of debate. The primary aim of the present study was to assess the 10-year survival free from revision in ankles that had undergone arthroscopic BMS for an OLT. The secondary aim was to evaluate the influence of baseline patient and lesion characteristics on survival.

METHODS

Patients who underwent arthroscopic BMS for a symptomatic OLT and had a minimum follow-up of 10 years were included to assess procedure survival. The primary outcome, the 10-year cumulative survival rate, was analyzed by the Kaplan-Meier survival method. Secondary outcomes were the median time to revision and the effects of baseline factors (lesion size, primary or non-primary lesion type, preoperative cysts, and obesity as defined by a body mass index [BMI] of ≥30 kg/m2) on survival, analyzed with a Cox regression model and reported using hazard ratios (HRs).

RESULTS

The 262 included patients had a mean follow-up of 15.3 ± 4.8 years. The 10-year cumulative survival rate of the arthroscopic BMS procedures was 82% (95% confidence interval [CI]: 77% to 87%). At 15 years of follow-up, the cumulative survival rate was 82% (95% CI: 76% to 86%). The median time to revision was 2.4 years (interquartile range: 1.3 to 5.1 years). Of the baseline factors, obesity (HR: 3.0 [95% CI: 1.44 to 6.43], p < 0.01) was associated with decreased survival. Lesion size (HR: 0.9 [95% CI: 0.5 to 1.8], p = 0.8), non-primary lesion type (HR: 1.8 [95% CI: 0.9 to 3.4], p = 0.1), and the presence of preoperative cysts (HR: 1.0 [95% CI: 0.6 to 1.9], p = 0.9) were not significantly associated with survival.

CONCLUSIONS

At a minimum follow-up of 10 years, the survival rate of arthroscopic BMS for OLT was 82%. At 15 and 20 years of follow-up, survival appeared to remain stable. Obesity (BMI ≥ 30 kg/m2) was associated with a higher likelihood of revision surgery. This risk factor should be incorporated into the treatment algorithm for OLT when counseling patients regarding surgery.

LEVEL OF EVIDENCE

Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.

External Validation of Updated Prediction Models for Neurological Outcomes at 90 Days in Patients With Out-of-Hospital Cardiac Arrest

BACKGROUND

Few prediction models for individuals with early-stage out-of-hospital cardiac arrest (OHCA) have undergone external validation. This study aimed to externally validate updated prediction models for OHCA outcomes using a large nationwide dataset.

METHODS AND RESULTS

We performed a secondary analysis of the JAAM-OHCA (Comprehensive Registry of In-Hospital Intensive Care for Out-of-Hospital Cardiac Arrest Survival and the Japanese Association for Acute Medicine Out-of-Hospital Cardiac Arrest) registry. Previously developed prediction models for patients with cardiac arrest who achieved the return of spontaneous circulation were updated. External validation was conducted using data from 56 institutions from the JAAM-OHCA registry. The primary outcome was a dichotomized 90-day cerebral performance category score. Two models were updated using the derivation set (n=3337). Model 1 included patient demographics, prehospital information, and the initial rhythm upon hospital admission; Model 2 included information obtained in the hospital immediately after the return of spontaneous circulation. In the validation set (n=4250), Models 1 and 2 exhibited a C-statistic of 0.945 (95% CI, 0.935-0.955) and 0.958 (95% CI, 0.951-0.960), respectively. Both models were well-calibrated to the observed outcomes. The decision curve analysis showed that Model 2 demonstrated higher net benefits at all risk thresholds than Model 1. A web-based calculator was developed to estimate the probability of poor outcomes (https://pcas-prediction.shinyapps.io/90d_lasso/).

CONCLUSIONS

The updated models offer valuable information to medical professionals in the prediction of long-term neurological outcomes for patients with OHCA, potentially playing a vital role in clinical decision-making processes.

Characterizing cell type specific transcriptional differences between the living and postmortem human brain

Single-nucleus RNA sequencing (snRNA-seq) is often used to define gene expression patterns characteristic of brain cell types as well as to identify cell type specific gene expression signatures of neurological and mental illnesses in postmortem human brains. As methods to obtain brain tissue from living individuals emerge, it is essential to characterize gene expression differences associated with tissue originating from either living or postmortem subjects using snRNA-seq, and to assess whether and how such differences may impact snRNA-seq studies of brain tissue. To address this, human prefrontal cortex single nuclei gene expression was generated and compared between 31 samples from living individuals and 21 postmortem samples. The same cell types were consistently identified in living and postmortem nuclei, though for each cell type, a large proportion of genes were differentially expressed between samples from postmortem and living individuals. Notably, estimation of cell type proportions by cell type deconvolution of pseudo-bulk data was found to be more accurate in samples from living individuals. To allow for future integration of living and postmortem brain gene expression, a model was developed that quantifies from gene expression data the probability a human brain tissue sample was obtained postmortem. These probabilities are established as a means to statistically account for the gene expression differences between samples from living and postmortem individuals. Together, the results presented here provide a deep characterization of both differences between snRNA-seq derived from samples from living and postmortem individuals, as well as qualify and account for their effect on common analyses performed on this type of data.

Predicting Progression from Gestational Diabetes to Impaired Glucose Tolerance Using Peridelivery Data: An Observational Study

OBJECTIVE

 This article aimed to develop a predictive model to identify persons with recent gestational diabetes mellitus (GDM) most likely to progress to impaired glucose tolerance postpartum.

STUDY DESIGN

 We conducted an observational study among persons with GDM in their most recent pregnancy, defined by Carpenter-Coustan criteria. Participants were followed up from delivery through 1-year postpartum. We used lasso regression with k-fold cross validation to develop a multivariable model to predict progression to impaired glucose tolerance, defined as HbA1c≥5.7%, at 1-year postpartum. Predictive ability was assessed by the area under the curve (AUC), sensitivity, specificity, and positive and negative predictive values (PPV and NPV).

RESULTS

 Of 203 participants, 71 (35%) had impaired glucose tolerance at 1-year postpartum. The final model had an AUC of 0.79 (95% confidence interval [CI]: 0.72, 0.85) and included eight indicators of weight, body mass index, family history of type 2 diabetes, GDM in a prior pregnancy, GDM diagnosis<24 weeks' gestation, and fasting and 2-hour plasma glucose at 2 days postpartum. A cutoff point of ≥ 0.25 predicted probability had sensitivity of 80% (95% CI: 69, 89), specificity of 58% (95% CI: 49, 67), PPV of 51% (95% CI: 41, 61), and NPV of 85% (95% CI: 76, 91) to identify women with impaired glucose tolerance at 1-year postpartum.

CONCLUSION

 Our predictive model had reasonable ability to predict impaired glucose tolerance around delivery for persons with recent GDM.

KEY POINTS

· We developed a predictive model to identify persons with GDM most likely to develop IGT postpartum.. · The final model had an AUC of 0.79 (95% CI: 0.72, 0.85) and included eight clinical indicators.. · If validated, our model could help prioritize diabetes prevention efforts among persons with GDM..

Developing a risk prediction model for death at first suicide attempt-Identifying risk factors from Thailand's national suicide surveillance system data

More than 60% of suicides globally are estimated to take place in low- and middle-income nations. Prior research on suicide has indicated that over 50% of those who die by suicide do so on their first attempt. Nevertheless, there is a dearth of knowledge on the attributes of individuals who die on their first attempt and the factors that can predict mortality on the first attempt in these regions. The objective of this study was to create an individual-level risk-prediction model for mortality on the first suicide attempt. We analyzed records of individuals' first suicide attempts that occurred between May 1, 2017, and April 30, 2018, from the national suicide surveillance system, which includes all of the provinces of Thailand. Subsequently, a risk-prediction model for mortality on the first suicide attempt was constructed utilizing multivariable logistic regression and presented through a web-based application. The model's performance was assessed by calculating the area under the receiver operating curve (AUC), as well as measuring its sensitivity, specificity, and accuracy. Out of the 3,324 individuals who made their first suicide attempt, 50.5% of them died as a result of that effort. Nine out of the 21 potential predictors demonstrated the greatest predictive capability. These included male sex, age over 50 years old, unemployment, having a depressive disorder, having a psychotic illness, experiencing interpersonal problems such as being aggressively criticized or desiring plentiful attention, having suicidal intent, and displaying suicidal warning signals. The model demonstrated a good predictive capability, with an AUC of 0.902, a sensitivity of 84.65%, a specificity of 82.66%, and an accuracy of 83.63%. The implementation of this predictive model can assist physicians in conducting comprehensive evaluations of suicide risk in clinical settings and devising treatment plans for preventive intervention.

QuantiFERON-CMV as a Predictor of CMV Events During Preemptive Therapy in CMV-seropositive Kidney Transplant Recipients

BACKGROUND

Prevention of cytomegalovirus (CMV) infection after kidney transplantation is costly and burdensome.

METHODS

Given its promising utility in risk stratification, we evaluated the use of QuantiFERON-CMV (QFCMV) and additional clinical variables in this prospective cohort study to predict the first clinically significant CMV infection (CS-CMV, ranging from asymptomatic viremia requiring treatment to CMV disease) in the first posttransplant year. A cost-effectiveness analysis for guided prevention was done.

RESULTS

One hundred adult kidney transplant recipients, CMV IgG + , were given basiliximab induction and maintained on steroid/mycophenolate/tacrolimus with weekly CMV monitoring. Thirty-nine patients developed CS-CMV infection (viral syndrome, n = 1; end-organ disease, n = 9; and asymptomatic viremia, n = 29). A nonreactive or indeterminate QFCMV result using the standard threshold around day 30 (but not before transplant) was associated with CS-CMV rates of 50% and 75%, respectively. A higher QFCMV threshold for reactivity (>1.0 IU interferon-γ/mL) outperformed the manufacturer's standard (>0.2 IU interferon-γ/mL) in predicting protection but still allowed a 16% incidence of CS-CMV. The combination of recipient age and type of donor, along with posttransplant QFCMV resulted in a prediction model that increased the negative predictive value from 84% (QFCMV alone) to 93%. QFCMV-guided preemptive therapy was of lower cost than preemptive therapy alone ( P  < 0.001, probabilistic sensitivity analysis) and was cost-effective (incremental net monetary benefit of 210 USD) assuming willingness-to-pay of 2000 USD to avoid 1 CMV disease.

CONCLUSIONS

Guided CMV prevention by the prediction model with QFCMV is cost-effective and would spare from CMV surveillance in 42% of patients with low risk for CS-CMV.

Acute kidney injury in hospitalized patients with nonmalignant pleural effusions: a retrospective cohort study

BACKGROUND

Nonmalignant pleural effusion (NMPE) is common and remains a definite health care problem. Pleural effusion was supposed to be a risk factor for acute kidney injury (AKI). Incidence of AKI in NMPE patients and whether there is correlation between the size of effusions and AKI is unknown.

OBJECTIVE

To assess the incidence of AKI in NMPE inpatients and its association with effusion size.

STUDY DESIGN AND METHOD

We conducted a retrospective cohort study of inpatients admitted to the Chinese PLA General Hospital with pleural effusion from 2018-2021. All patients with pleural effusions confirmed by chest radiography (CT or X-ray) were included, excluding patients with diagnosis of malignancy, chronic dialysis, end-stage renal disease (ESRD), community-acquired AKI, hospital-acquired AKI before chest radiography, and fewer than two serum creatinine tests during hospitalization. Multivariate logistic regression and LASSO logistic regression models were used to identify risk factors associated with AKI. Subgroup analyses and interaction tests for effusion volume were performed adjusted for the variables selected by LASSO. Causal mediation analysis was used to estimate the mediating effect of heart failure, pneumonia, and eGFR < 60 ml/min/1.73m2 on AKI through effusion volume.

RESULTS

NMPE was present in 7.8% of internal medicine inpatients. Of the 3047 patients included, 360 (11.8%) developed AKI during hospitalization. After adjustment by covariates selected by LASSO, moderate and large effusions increased the risk of AKI compared with small effusions (moderate: OR 1.47, 95%CI 1.11-1.94 p = 0.006; large: OR 1.86, 95%CI 1.05-3.20 p = 0.028). No significant modification effect was observed among age, gender, diabetes, bilateral effusions, and eGFR. Volume of effusions mediated 6.8% (p = 0.005), 4.0% (p = 0.046) and 4.6% (p < 0.001) of the effect of heart failure, pneumonia and low eGFR on the development of AKI respectively.

CONCLUSION

The incidence of AKI is high among NMPE patients. Moderate and large effusion volume is independently associated with AKI compared to small size. The effusion size acts as a mediator in heart failure, pneumonia, and eGFR.

The evaluation of frequency and predictors of delirium and its short-term and long-term outcomes in hospitalized older adults'

BACKGROUND

Delirium is a common complication in hospitalized older adults with multifactorial etiology and poor health outcomes.

AIM

To determine the frequency and predictors of delirium and its short-term and long-term outcomes in hospitalized older adults.

METHODS

A prospective observational study was performed in patients aged ≥60 years consecutively admitted to geriatric ward. Potential risk factors were assessed within 24 hours of hospital admission. Delirium screening was performed on admission and daily thereafter throughout the hospital stay using Confusion Assessment Method (CAM). Patients were followed up at 1-year post-discharge.

RESULTS

The study included 200 patients with mean age 73.1 ± 8.83 years. Incidence and prevalence rate of delirium were 5% and 20% respectively. Multivariable regression analysis revealed emergency admission (OR= 5.12 (1.94-13.57), p=0.001), functional dependency (Katz index of Independence in Activities of Daily Living (Katz-ADL) score <5) 2 weeks before admission (OR= 3.08 (1.30-7.33), p=0.011) and more psychopathological symptoms (higher Brief Psychiatric Rating Scale (BPRS) total score) (OR=1.12 (1.06-1.18), p=0.001) to be independently associated with delirium. Patients in delirium group had significantly high in-hospital mortality (OR= 5.02 (2.12-11.8), p=0.001) and post-discharge mortality (HR= 2.02 (1.13-3.61), p=0.017) and functional dependency (Katz-ADL score <5) (OR= 5.45 (1.49-19.31), p=0.01) at 1-year follow up.

CONCLUSION

Delirium is quite frequent in geriatric inpatients and is associated with high in-hospital and post-discharge mortality risk and long-term functional dependency. Emergency admission, pre-hospitalization functional dependency, and more general psychopathological symptoms are independently associated factors. Hence, earliest identification and treatment with early implementation of rehabilitation services is warranted.

Development of a simple prediction model for mechanical complication in ST-segment elevation myocardial infarction patients after primary percutaneous coronary intervention

Mechanical complication (MC) is a rare but serious complication in patients with ST-segment elevation myocardial infarction (STEMI). Although several risk factors for MC have been reported, a prediction model for MC has not been established. This study aimed to develop a simple prediction model for MC after STEMI. We included 1717 patients with STEMI who underwent primary percutaneous coronary intervention (PCI). Of 1717 patients, 45 MCs occurred after primary PCI. Prespecified predictors were determined to develop a tentative prediction model for MC using multivariable regression analysis. Then, a simple prediction model for MC was generated. Age ≥ 70, Killip class ≥ 2, white blood cell ≥ 10,000/µl, and onset-to-visit time ≥ 8 h were included in a simple prediction model as "point 1" risk score, whereas initial thrombolysis in myocardial infarction (TIMI) flow grade ≤ 1 and final TIMI flow grade ≤ 2 were included as "point 2" risk score. The simple prediction model for MC showed good discrimination with the optimism-corrected area under the receiver-operating characteristic curve of 0.850 (95% CI: 0.798-0.902). The predicted probability for MC was 0-2% in patients with 0-4 points of risk score, whereas that was 6-50% in patients with 5-8 points. In conclusion, we developed a simple prediction model for MC. We may be able to predict the probability for MC by this simple prediction model.

Concurrent JCPyV-DNAemia Is Correlated With Poor Graft Outcome in Kidney Transplant Recipients with Polyomavirus-associated Nephropathy

BACKGROUND

Co-infection of JC polyomavirus (JCPyV) and BK polyomavirus (BKPyV) is uncommon in kidney transplant recipients, and the prognosis is unclear. This study aimed to investigate the effect of concurrent JCPyV-DNAemia on graft outcomes in BKPyV-infected kidney transplant recipients with polyomavirus-associated nephropathy (PyVAN).

METHODS

A total of 140 kidney transplant recipients with BKPyV replication and PyVAN, 122 without concurrent JCPyV-DNAemia and 18 with JCPyV-DNAemia were included in the analysis. Least absolute shrinkage and selection operator regression analysis and multivariate Cox regression analysis were used to identify prognostic factors for graft survival. A nomogram for predicting graft survival was created and evaluated.

RESULTS

The median tubulitis score in the JCPyV-DNAemia-positive group was higher than in JCPyV-DNAemia-negative group (P = 0.048). At last follow-up, the graft loss rate in the JCPyV-DNAemia-positive group was higher than in the JCPyV-DNAemia-negative group (50% versus 25.4%; P = 0.031). Kaplan-Meier analysis showed that the graft survival rate in the JCPyV-DNAemia-positive group was lower than in the JCPyV-DNAemia-negative group (P = 0.003). Least absolute shrinkage and selection operator regression and multivariate Cox regression analysis demonstrated that concurrent JCPyV-DNAemia was an independent risk factor for graft survival (hazard ratio = 4.808; 95% confidence interval: 2.096-11.03; P < 0.001). The nomogram displayed favorable discrimination (C-index = 0.839), concordance, and clinical applicability in predicting graft survival.

CONCLUSIONS

Concurrent JCPyV-DNAemia is associated with a worse graft outcome in BKPyV-infected kidney transplant recipients with PyVAN.

Prediction of vascular complications in free flap reconstruction with machine learning

OBJECTIVE

This study aims to explore the risk factors of vascular complications following free flap reconstruction and to develop a clinical auxiliary assessment tool for predicting vascular complications in patients undergoing free flap reconstruction leveraging machine learning methods.

METHODS

We reviewed the medical data of patients who underwent free flap reconstruction at the Affiliated Hospital of Zunyi Medical University retrospectively from January 1, 2019, to December 31, 2021. Statistical analysis was used to screen risk factors. A training data set was generated and augmented using the synthetic minority oversampling technique. Logistic regression, random forest and neural network, models were trained, using this dataset. The performance of these three predictive models was then evaluated and compared using a test set, with four metrics, area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

RESULTS

A total of 570 patients who underwent free flap reconstruction were included in this study, 46 of whom developed postoperative vascular complications. Among the models tested, the neural network model exhibited superior performance on the test set, achieving an AUC of 0.828. Multivariate logistic regression analysis identified that preoperative hemoglobin levels, preoperative fibrinogen levels, operation duration, smoking history, the number of anastomoses, and peripheral vascular injury as statistically significant independent risk factors for vascular complications post-free flap reconstruction. The top five predictive factors in the neural network were fibrinogen content, operation duration, donor site, body mass index (BMI), and platelet count.

CONCLUSION

Hemoglobin levels, fibrinogen levels, operation duration, smoking history, and anastomotic veins are independent risk factors for vascular complications following free flap reconstruction. These risk factors enhance the ability of machine learning models to predict the occurrence of vascular complications and identify high-risk patients. The neural network model outperformed the logistic regression and random forest models, suggesting its potential to aid clinicians in early identification of high-risk patients thereby mitigating patient suffering and improving prognosis.

Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers

BACKGROUND

This study aimed to develop and validate an interpretable machine-learning model that utilizes clinical features and inflammatory biomarkers to predict the risk of in-hospital mortality in critically ill patients suffering from sepsis.

METHODS

We enrolled all patients diagnosed with sepsis in the Medical Information Mart for Intensive Care IV (MIMIC-IV, v.2.0), eICU Collaborative Research Care (eICU-CRD 2.0), and the Amsterdam University Medical Centers databases (AmsterdamUMCdb 1.0.2). LASSO regression was employed for feature selection. Seven machine-learning methods were applied to develop prognostic models. The optimal model was chosen based on its accuracy, F1 score and area under curve (AUC) in the validation cohort. Moreover, we utilized the SHapley Additive exPlanations (SHAP) method to elucidate the effects of the features attributed to the model and analyze how individual features affect the model's output. Finally, Spearman correlation analysis examined the associations among continuous predictor variables. Restricted cubic splines (RCS) explored potential non-linear relationships between continuous risk factors and in-hospital mortality.

RESULTS

3535 patients with sepsis were eligible for participation in this study. The median age of the participants was 66 years (IQR, 55-77 years), and 56% were male. After selection, 12 of the 45 clinical parameters collected on the first day after ICU admission remained associated with prognosis and were used to develop machine-learning models. Among seven constructed models, the eXtreme Gradient Boosting (XGBoost) model achieved the best performance, with an AUC of 0.94 and an F1 score of 0.937 in the validation cohort. Feature importance analysis revealed that Age, AST, invasive ventilation treatment, and serum urea nitrogen (BUN) were the top four features of the XGBoost model with the most significant impact. Inflammatory biomarkers may have prognostic value. Furthermore, SHAP force analysis illustrated how the constructed model visualized the prediction of the model.

CONCLUSIONS

This study demonstrated the potential of machine-learning approaches for early prediction of outcomes in patients with sepsis. The SHAP method could improve the interoperability of machine-learning models and help clinicians better understand the reasoning behind the outcome.

Risk Factors for Surgical Site Infection after Lower Limb Revascularisation Surgery: a Systematic Review and Meta-Analysis of Prognostic Studies

OBJECTIVE

To systematically review and meta-analyse adjusted risk factors for surgical site infection (SSI) after lower limb revascularisation surgery.

DATA SOURCES

MEDLINE, Embase, Evidence Based Medicine Reviews, and the Cochrane Central Register of Controlled Trials (inception to 28 April 2022).

REVIEW METHODS

Systematic review and meta-analysis conducted according to PRISMA guidelines. After protocol registration, databases were searched. Studies reporting adjusted risk factors for SSI in adults who underwent lower limb revascularisation surgery for peripheral artery disease were included. Adjusted odds ratios (ORs) were pooled using random effects models. GRADE was used to assess certainty.

RESULTS

Among 6 377 citations identified, 50 studies (n = 271 125 patients) were included. The cumulative incidence of SSI was 12 (95% confidence interval [CI] 10 - 13) per 100 patients. Studies reported 139 potential SSI risk factors adjusted for a median of 12 (range 1 - 69) potential confounding factors. Risk factors that increased the pooled adjusted odds of SSI included: female sex (pooled OR 1.41, 95% CI 1.20 - 1.64; high certainty); dependent functional status (pooled OR 1.18, 95% CI 1.03 - 1.35; low certainty); being overweight (pooled OR 1.82, 95% CI 1.29 - 2.56; moderate certainty), obese (pooled OR 2.20, 95% CI 1.44 - 3.36; high certainty), or morbidly obese (pooled OR 1.65, 95% CI 1.08 - 2.52; moderate certainty); chronic obstructive pulmonary disease (pooled OR 1.42, 95% CI 1.17 - 1.71; high certainty); chronic limb threatening ischaemia (pooled OR 1.67, 95% CI 1.22 - 2.29; moderate certainty); chronic kidney disease (pooled OR 2.13, 95% CI 1.18 - 3.83; moderate certainty); intra-operative (pooled OR 1.23, 95% CI 1.02 - 1.49), peri-operative (pooled OR 1.92, 95% CI 1.27 - 2.90), or post-operative (pooled OR 2.21, 95% CI 1.44 - 3.39) blood transfusion (moderate certainty for all); urgent or emergency surgery (pooled OR 2.12, 95% CI 1.22 - 3.70; moderate certainty); vein bypass and or patch instead of endarterectomy alone (pooled OR 1.86, 95% CI 1.33 - 2.59; moderate certainty); an operation lasting ≥ 3 hours (pooled OR 1.86, 95% CI 1.33 - 2.59; moderate certainty) or ≥ 5 hours (pooled OR 1.60, 95% CI 1.18 - 2.17; moderate certainty); and early or unplanned re-operation (pooled OR 4.50, 95% CI 2.18 - 9.32; low certainty).

CONCLUSION

This systematic review identified evidence informed SSI risk factors following lower limb revascularisation surgery. These may be used to develop improved SSI risk prediction tools and to identify patients who may benefit from evidence informed SSI prevention strategies.

Examining factors influencing the severity of motorized two-wheeler crashes in Delhi

Failure to meet road safety targets has necessitated urgent actions from stakeholders worldwide, especially in developing countries like India. Road safety of motorized two-wheelers (MTWs), one of India's most preferred travel modes for urban commutes, is in danger and witnessing threatening figures of fatalities and injuries. Most of the studies in the domain of MTW safety were conducted in developed countries, with very limited research in countries having a significant proportion of MTWs. The present work investigates police-reported crash data to identify the contributory factors of motorized two-wheeler crash severity. Data from MTW crash-prone areas were selected from Delhi, which is leading in road traffic fatalities among the million-plus urban cities in India. A binary logistic regression model was developed using the data for 2016-2018 period. The model results show that the odds of fatal motorized two-wheeler crashes increase when the following circumstances apply: crash occurs on underpasses; involves bus, truck, heavy motor vehicle (lorry, crane) as the striking vehicle; when hit-and-run type of crash occurs and when older age-group (> = 55) riders are involved. Finally, based on the findings, countermeasures were suggested to facilitate policymakers and traffic enforcement agencies, in improving the road safety situation of MTW users.

Modifiable prognostic factors of high societal costs among people on sick leave due to musculoskeletal disorders: findings from an occupational cohort study

OBJECTIVES

The objective was to identify modifiable prognostic factors of high societal costs among people on sick leave due to musculoskeletal disorders, and to identify modifiable prognostic factors of high costs related to separately healthcare utilisation and productivity loss.

DESIGN

A prospective cohort study with a 1-year follow-up.

PARTICIPANTS AND SETTING

A total of 549 participants (aged 18-67 years) on sick leave (≥ 4 weeks) due to musculoskeletal disorders in Norway were included.

OUTCOME MEASURES AND METHOD

The primary outcome was societal costs aggregated for 1 year of follow-up and dichotomised as high or low, defined by the top 25th percentile. Secondary outcomes were high costs related to separately healthcare utilisation and productivity loss aggregated for 1 year of follow-up. Healthcare utilisation was collected from public records and included primary, secondary and tertiary healthcare use. Productivity loss was collected from public records and included absenteeism, work assessment allowance and disability pension. Nine modifiable prognostic factors were selected based on previous literature. Univariable and multivariable binary logistic regression analyses were performed to identify associations (crude and adjusted for selected covariates) between each modifiable prognostic factor and having high costs.

RESULTS

Adjusted for selected covariates, six modifiable prognostic factors associated with high societal costs were identified: pain severity, disability, self-perceived health, sleep quality, return to work expectation and long-lasting disorder expectation. Depressive symptoms, work satisfaction and health literacy showed no prognostic value. More or less similar results were observed when high costs were related to separately healthcare utilisation and productivity loss.

CONCLUSION

Factors identified in this study are potential target areas for interventions which could reduce high societal costs among people on sick leave due to musculoskeletal disorders. However, future research aimed at replicating these findings is warranted.

TRIAL REGISTRATION NUMBER

NCT04196634, 12 December 2019.

Tumor biomarkers in evaluating the severity and prognosis of idiopathic pulmonary arterial hypertension: A comprehensive analysis

Inflammation contributes to development of idiopathic pulmonary arterial hypertension (IPAH), and tumor biomarkers can reflect inflammatory and immune status. We aimed to determine the value of tumor biomarkers in IPAH comprehensively. We enrolled 315 patients with IPAH retrospectively. Tumor biomarkers were correlated with established indicators of pulmonary hypertension severity. Multivariable Cox regression found that AFP (hazard ratio [HR]: 1.587, 95% confidence interval [CI]: 1.014-2.482, p = 0.043) and CA125 (HR: 2.018, 95% CI: 1.163-3.504, p = 0.013) could independently predict prognosis of IPAH. The changes of AFP over time were associated with prognosis of patients, each 1 ng/mL increase in AFP was associated with 5.4% increased risk of clinical worsening (HR: 1.054, 95% CI: 1.001-1.110, p = 0.046), enabling detection of disease progression. Moreover, beyond well-validated PH biomarkers, CA125 was still of prognostic value in the low-risk patients (HR: 1.014, 95% CI: 1.004-1.024, p = 0.004), allowing for more accurate risk stratification and prediction of disease outcomes. AFP and CA125 can serve for prognosis prediction, risk stratification, and dynamic monitor in patients with IPAH.

Investigation of statistical methods used in prognostic prediction models for obstetric care: A 10 year-span cross-sectional study

INTRODUCTION

Obstetric care is a highly active area in the development and application of prognostic prediction models. The development and validation of these models often require the utilization of advanced statistical techniques. However, failure to adhere to rigorous methodological standards could greatly undermine the reliability and trustworthiness of the resultant models. Consequently, the aim of our study was to examine the current statistical practices employed in obstetric care and offer recommendations to enhance the utilization of statistical methods in the development of prognostic prediction models.

MATERIAL AND METHODS

We conducted a cross-sectional survey using a sample of studies developing or validating prognostic prediction models for obstetric care published in a 10-year span (2011-2020). A structured questionnaire was developed to investigate the statistical issues in five domains, including model derivation (predictor selection and algorithm development), model validation (internal and external), model performance, model presentation, and risk threshold setting. On the ground of survey results and existing guidelines, a list of recommendations for statistical methods in prognostic models was developed.

RESULTS

A total of 112 eligible studies were included, with 107 reporting model development and five exclusively reporting external validation. During model development, 58.9% of the studies did not include any form of validation. Of these, 46.4% used stepwise regression in a crude manner for predictor selection, while two-thirds made decisions on retaining or dropping candidate predictors solely based on p-values. Additionally, 26.2% transformed continuous predictors into categorical variables, and 80.4% did not consider nonlinear relationships between predictors and outcomes. Surprisingly, 94.4% of the studies did not examine the correlation between predictors. Moreover, 47.1% of the studies did not compare population characteristics between the development and external validation datasets, and only one-fifth evaluated both discrimination and calibration. Furthermore, 53.6% of the studies did not clearly present the model, and less than half established a risk threshold to define risk categories. In light of these findings, 10 recommendations were formulated to promote the appropriate use of statistical methods.

CONCLUSIONS

The use of statistical methods is not yet optimal. Ten recommendations were offered to assist the statistical methods of prognostic prediction models in obstetric care.

The collateral map: prediction of lesion growth and penumbra after acute anterior circulation ischemic stroke

OBJECTIVES

This study evaluated the collateral map's ability to predict lesion growth and penumbra after acute anterior circulation ischemic strokes.

METHODS

This was a retrospective analysis of selected data from a prospectively collected database. The lesion growth ratio was the ratio of the follow-up lesion volume to the baseline lesion volume on diffusion-weighted imaging (DWI). The time-to-maximum (Tmax)/DWI ratio was the ratio of the baseline Tmax  > 6 s volume to the baseline lesion volume. The collateral ratio was the ratio of the hypoperfused lesion volume of the phase_FU (phase with the hypoperfused lesions most approximate to the follow-up DWI lesion) to the hypoperfused lesion volume of the phase_baseline of the collateral map. Multiple logistic regression analyses were conducted to identify independent predictors of lesion growth. The concordance correlation coefficients of Tmax/DWI ratio and collateral ratio for lesion growth ratio were analyzed.

RESULTS

Fifty-two patients, including twenty-six males (mean age, 74 years), were included. Intermediate (OR, 1234.5; p < 0.001) and poor collateral perfusion grades (OR, 664.7; p = 0.006) were independently associated with lesion growth. Phase_FUs were immediately preceded phases of the phase_baselines in intermediate or poor collateral perfusion grades. The concordance correlation coefficients of the Tmax/DWI ratio and collateral ratio for the lesion growth ratio were 0.28 (95% CI, 0.17-0.38) and 0.88 (95% CI, 0.82-0.92), respectively.

CONCLUSION

Precise prediction of lesion growth and penumbra can be possible using collateral maps, allowing for personalized application of recanalization treatments. Further studies are needed to generalize the findings of this study.

CLINICAL RELEVANCE STATEMENT

Precise prediction of lesion growth and penumbra can be possible using collateral maps, allowing for personalized application of recanalization treatments.

KEY POINTS

• Cell viability in cerebral ischemia due to proximal arterial steno-occlusion mainly depends on the collateral circulation. • The collateral map shows salvageable brain extent, which can survive by recanalization treatments after acute anterior circulation ischemic stroke. • Precise estimation of salvageable brain makes it possible to make patient-specific treatment decision.

Denosumab improves trabecular bone score in relationship with decrease in fracture risk of women exposed to aromatase inhibitors

PURPOSE

Trabecular bone score (TBS) is a gray-level textural metric that has shown to correlate with risk of fractures in several forms of osteoporosis. The value of TBS in predicting fractures and the effects of bone-active drugs on TBS in aromatase inhibitors (AIs)-induced osteoporosis are still largely unknown. The primary objective of this retrospective study was to assess the effects of denosumab and bisphosphonates (BPs) on TBS and vertebral fractures (VFs) in women exposed to AIs.

METHODS

241 consecutive women (median age 58 years) with early breast cancer undergoing treatment with AIs were evaluated for TBS, bone mineral density (BMD) and morphometric VFs at baseline and after 18-24 months of follow-up. During the study period, 139 women (57.7%) received denosumab 60 mg every 6 months, 53 (22.0%) BPs, whereas 49 women (20.3%) were not treated with bone-active drugs.

RESULTS

Denosumab significantly increased TBS values (from 1.270 to 1.323; P < 0.001) accompanied by a significant decrease in risk of VFs (odds ratio 0.282; P = 0.021). During treatment with BPs, TBS did not significantly change (P = 0.849) and incidence of VFs was not significantly different from women untreated with bone-active drugs (P = 0.427). In the whole population, women with incident VFs showed higher decrease in TBS vs. non-fractured women (P = 0.003), without significant differences in changes of BMD at any skeletal site.

CONCLUSIONS

TBS variation predicts fracture risk in AIs treated women. Denosumab is effective to induce early increase of TBS and reduction in risk of VFs.

Development and internal validation of a clinical prediction model for serious complications after emergency laparotomy

PURPOSE

Emergency laparotomy (EL) is a common operation with high risk for postoperative complications, thereby requiring accurate risk stratification to manage vulnerable patients optimally. We developed and internally validated a predictive model of serious complications after EL.

METHODS

Data for eleven carefully selected candidate predictors of 30-day postoperative complications (Clavien-Dindo grade >  = 3) were extracted from the HELAS cohort of EL patients in 11 centres in Greece and Cyprus. Logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO) was applied for model development. Discrimination and calibration measures were estimated and clinical utility was explored with decision curve analysis (DCA). Reproducibility and heterogeneity were examined with Bootstrap-based internal validation and Internal-External Cross-Validation. The American College of Surgeons National Surgical Quality Improvement Program's (ACS-NSQIP) model was applied to the same cohort to establish a benchmark for the new model.

RESULTS

From data on 633 eligible patients (175 complication events), the SErious complications After Laparotomy (SEAL) model was developed with 6 predictors (preoperative albumin, blood urea nitrogen, American Society of Anaesthesiology score, sepsis or septic shock, dependent functional status, and ascites). SEAL had good discriminative ability (optimism-corrected c-statistic: 0.80, 95% confidence interval [CI] 0.79-0.81), calibration (optimism-corrected calibration slope: 1.01, 95% CI 0.99-1.03) and overall fit (scaled Brier score: 25.1%, 95% CI 24.1-26.1%). SEAL compared favourably with ACS-NSQIP in all metrics, including DCA across multiple risk thresholds.

CONCLUSION

SEAL is a simple and promising model for individualized risk predictions of serious complications after EL. Future external validations should appraise SEAL's transportability across diverse settings.

Filamin A is involved in human intrahepatic cholangiocarcinoma aggressiveness and progression

BACKGROUND & AIMS

Intrahepatic cholangiocarcinoma (iCCA) is a primary liver tumour, characterized by poor prognosis and lack of effective therapy. The cytoskeleton protein Filamin A (FLNA) is involved in cancer progression and metastasis, including primary liver cancer. FLNA is cleaved by calpain, producing a 90 kDa fragment (FLNACT ) that can translocate to the nucleus and inhibit gene transcription. We herein aim to define the role of FLNA and its cleavage in iCCA carcinogenesis.

METHODS & RESULTS

We evaluated the expression and localization of FLNA and FLNACT in liver samples from iCCA patients (n = 82) revealing that FLNA expression was independently correlated with disease-free survival. Primary tumour cells isolated from resected iCCA patients expressed both FLNA and FLNACT , and bulk RNA sequencing revealed a significant enrichment of cell proliferation and cell motility pathways in iCCAs with high FLNA expression. Further, we defined the impact of FLNA and FLNACT on the proliferation and migration of primary iCCA cells (n = 3) and HuCCT1 cell line using silencing and Calpeptin, a calpain inhibitor. We observed that FLNA silencing decreased cell proliferation and migration and Calpeptin was able to reduce FLNACT expression in both the HuCCT1 and iCCA cells (p < .05 vs. control). Moreover, Calpeptin 100 μM decreased HuCCT1 and primary iCCA cell proliferation (p <.00001 vs. control) and migration (p < .05 vs. control).

CONCLUSIONS

These findings demonstrate that FLNA is involved in human iCCA progression and calpeptin strongly decreased FLNACT expression, reducing cell proliferation and migration.

Pyroptosis-Related Gene Signature Predicts Prognosis and Response to Immunotherapy and Medication in Pediatric and Young Adult Osteosarcoma Patients

Purpose

Pyroptosis, a new form of inflammatory programmed cell death, has recently gained attention. However, the impact of the expression levels of pyroptosis-related genes (PRGs) on the overall survival (OS) of osteosarcoma patients remains unclear. This study aims to investigate the impact of the expression levels of PRGs on the OS of pediatric and young adult patients with osteosarcoma.

Patients and Methods

Transcriptome matrix datasets of normal muscle or skeletal tissues from the Genotype-Tissue Expression (GTEx) project and osteosarcoma specimen the National Cancer Institute's (NCI) Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were used to identify pyroptosis-related genes (PRGs) associated with prognosis. The National Center for Biotechnology Information's (NCBI) GSE21257 dataset was employed to validate the predictive value of the pyroptosis-related signature (PRS). Additionally, reverse transcription polymerase chain reaction (RT-qPCR) experiment was performed in normal and osteosarcoma cell lines.

Results

The study identified 18 differentially expressed PRGs (DEPRGs) between normal muscle or skeletal tissues and tumor samples. Multiple machine learning techniques were used to select PRGs, resulting in the identification of four hub PRGs. A PRS-score was calculated for each sample based on the expression of these four hub PRGs, and samples were categorized into low and high PRS-score level groups. It was confirmed that metastatic status and PRS-score level are independent prognostic predictors. A nomogram model for predicting OS of osteosarcoma patients was constructed. Single-cell RNA-sequencing data display the expression patterns of the hub PRGs. RT-qPCR data results were found to be consistent with the differential expression analysis performed on TARGET and GTEx samples.

Conclusion

The study developed a novel pyroptosis-related gene signature that can stratify pediatric and young adult osteosarcoma patients into different risk groups, thus predicting their response to immunotherapy and chemotherapy.

Long-term functional disabilities in intensive care unit survivors: A prospective cohort study

BACKGROUND

Functional disabilities are common in intensive care unit (ICU) survivors and may affect their ability to live independently. Few previous studies have investigated long-term functional outcomes with health status before ICU admission (pre-ICU health), and they are limited to specific patient groups.

OBJECTIVES

The objective of this study was to investigate the prevalence of functional disabilities and examine pre-ICU health variables as possible predictive factors of functional disabilities 12 months after ICU admission in a mixed population of ICU survivors.

METHODS

This prospective cohort study was conducted in six ICUs in Norway. Data on pre-ICU health were collected as soon as possible after ICU admission using patients, proxies, and patient electronic health records and at 12 months after ICU admission. Self-reported functional status was assessed using the Katz Index of independence in personal activities of daily living (P-ADL) and the Lawton instrumental activities of daily living scale (I-ADL).

RESULTS

A total of 220 of 343 (64%) ICU survivors with data on pre-ICU health completed the questionnaires at 12 months and reported the following functional disabilities at 12 months: 31 patients (14.4%) reported P-ADL dependencies (new in 16 and persisting in 15), and 80 patients (36.4%) reported I-ADL dependencies (new in 41 and persisting in 39). In a multivariate analysis, worse baseline P-ADL and I-ADL scores were associated with dependencies in P-ADLs (odds ratio [OR]: 1.87; 95% confidence interval [CI]: 1.14-3.06) and I-ADLs (OR: 1.52; 95% CI: 1.03-2.23), respectively, at 12 months. Patients who were employed were less likely to report I-ADL dependencies at 12 months (OR: 0.34; 95% CI: 0.12-0.95).

CONCLUSION

In a subsample of ICU survivors, patients reported functional disabilities 12 months after ICU admission, which was significantly associated with their pre-ICU functional status. Early screening of pre-ICU functional status may help identify patients at risk of long-term functional disabilities. ICU survivors with pre-ICU functional disabilities may find it difficult to improve their functional status.

Home Hospital Outcomes for Acute Decompensated Heart Failure and Factors Associated With Escalation of Care

BACKGROUND

Overall outcomes and the escalation rate for home hospital admissions for heart failure (HF) are not known. We report overall outcomes, predict escalation, and describe care provided after escalation among patients admitted to home hospital for HF.

METHODS

Our retrospective analysis included all patients admitted for HF to 2 home hospital programs in Massachusetts between February 2020 and October 2022. Escalation of care was defined as transfer to an inpatient hospital setting (emergency department, inpatient medical unit) for at least 1 overnight stay. Unexpected mortality was defined as mortality excluding those who desired to pass away at home on admission or transitioned to hospice. We performed the least absolute shrinkage and selection operator logistic regression to predict escalation.

RESULTS

We included 437 hospitalizations; patients had a median age of 80 (interquartile range, 69-89) years, 58.1% were women, and 64.8% were White. Of the cohort, 29.2% had reduced ejection fraction, 50.9% had chronic kidney disease, and 60.6% had atrial fibrillation. Median admission Get With The Guidelines HF score was 39 (interquartile range, 35-45; 1%-5% predicted inpatient mortality). Escalation occurred in 10.3% of hospitalizations. Thirty-day readmission occurred in 15.1%, 90-day readmission occurred in 33.8%, and 6-month mortality occurred in 11.5%. There was no unexpected mortality during home hospitalization. Patients who experienced escalation had significantly longer median length of stays (19 versus 7.5 days, P<0.001). The most common reason for escalation was progressive renal dysfunction (36.2%). A low mean arterial pressure at the time of admission to home hospital was the most significant predictor of escalation in the least absolute shrinkage and selection operator regression.

CONCLUSIONS

About 1 in 10 home hospital patients with HF required escalation; none had unexpected mortality. Patients requiring escalation had longer length of stays. A low mean arterial pressure at the time of admission to home hospital was the most important predictor of escalation of care in the least absolute shrinkage and selection operator logistic regression model.

Challenges and pitfalls in classification of disproportionate mitral regurgitation

The concept of disproportionate mitral regurgitation (dispropMR) has been introduced to identify patients with functional mitral regurgitation (MR) who benefit from percutaneous treatment. We aimed to examine echocardiographic characteristics behind this entity. We retrospectively included 172 consecutive patients with reduced left ventricular ejection fraction (LVEF), and more than mild MR referred to clinically indicated echocardiography. According to the proportionality ratio (effective regurgitant orifice area (EROA)/left ventricular end-diastolic volume (LVEDV)) patients were divided into dispropMR and proportionate MR (propMR) group. Potential factors which might affect proportionality definition were analyzed. 55 patients (32%) had dispropMR. Discrepant grading of MR severity was observed when using regurgitant volume (RegVol) by proximal isovelocity surface area (PISA) method or volumetric method, with significant discordance only in dispropMR (p < 0.001). Patients with dispropMR had more frequently left ventricular foreshortened images for LVEDV calculation than patients with propMR (p = 0.003), resulting in smaller LVEDV in dispropMR group. DispropMR group had more substantial dynamic variation of regurgitant flow compared to propMR. Accordingly, EROA was consistently overestimated by standard single-point PISA method compared to serial PISA method. This was more pronounced in dispropMR (bias:10.5 ± 28.3 mm2) compared to propMR group (bias:6.4 ± 12.8 mm2). DispropMR may be found in roughly one third of clinically indicated echocardiographic studies in patients with reduced LVEF and more than mild MR. EROA overestimation due to dynamic variation of regurgitant flow and LVEDV underestimation due to LV foreshortening were more frequently found in dispropMR. Our results indicate that methodological limitations of echocardiographic MR grading could not be neglected in classifying the proportionality of MR.

Establishment and Validation of a Risk Prediction Model for Mortality in Patients with Acinetobacter baumannii Infection: A Retrospective Study

Purpose

This study aims to establish a valuable risk prediction model for mortality in patients with Acinetobacter baumannii (A. baumannii).

Patients and Methods

The 622 patients with A. baumannii infection from the First Affiliated Hospital of Anhui Medical University were enrolled as the study cohort. Univariate and multivariate logistic regression analysis was used to preliminarily screen the independent risk factors of death caused by A. baumannii infection, followed by LASSO regression analysis to determine the risk factors. According to the calculated regression coefficient, the Nomogram death prediction model is established. The area under the curve (AUC) and decision curve analysis (DCA) of the operating characteristic (ROC) curve of the subjects are used to evaluate the discrimination of the established prediction model. The calibration degree of the prediction model is represented by a calibration chart. A validation cohort that consisted of 477 patients admitted to the 901st Hospital was also included.

Results

Our results revealed that the source of infection, carbapenem-resistant A. baumannii, mechanical ventilation, serum albumin value, and Charlson comorbidity index were independent risk factors for death caused by A. baumannii infection. The AUC value of ROC curves of study cohort and validation cohort were 0.76 and 0.69, respectively. The probability range (30-80%) indicated a high net income of the modified model and strong capacity of discrimination. The calibration curve obtained by analysis swings up and down around the 45 diagonal line, which shows that the calibration degree of the prediction model is very high.

Conclusion

In this study, we have reconstructed a risk prediction model for mortality in patients with A. baumannii infections. This model provides useful information to predict the risk of death in patients with A. baumannii infection, but the specificity is not optimistic. If this prediction model is wanted to be applied to clinical practice, more analysis and research are necessary.

Coronary artery disease in atrial fibrillation ablation: impact on arrhythmic outcomes

AIMS

Catheter ablation (CA) is an established treatment for atrial fibrillation (AF). A computed tomography (CT) may be performed before ablation to evaluate the anatomy of pulmonary veins. The aim of this study is to investigate the prevalence of patients with coronary artery disease (CAD) detected by cardiac CT scan pre-ablation and to evaluate the impact of CAD and revascularization on outcomes after AF ablation.

METHODS AND RESULTS

All consecutive patients with AF diagnosis, hospitalized at Universitair Ziekenhuis Brussel, Belgium, between 2015 and 2019, were prospectively screened for enrolment in the study. Inclusion criteria were (i) AF diagnosis, (ii) first procedure of AF ablation with cryoballoon CA, and (iii) contrast CT scan performed pre-ablation. A total of 576 consecutive patients were prospectively included and analysed in this study. At CT scan, 122 patients (21.2%) were diagnosed with CAD, of whom 41 patients (7.1%) with critical CAD. At survival analysis, critical CAD at CT scan was a predictor of atrial tachyarrhythmia (AT) recurrence during the follow-up, only in Cox univariate analysis [hazard ratio (HR) = 1.79] but was not an independent predictor in Cox multivariate analysis. At Cox multivariate analysis, independent predictors of AT recurrence were as follows: persistent AF (HR = 2.93) and left atrium volume index (HR = 1.04).

CONCLUSION

In patients undergoing CT scan before AF ablation, critical CAD was diagnosed in 7.1% of patients. Coronary artery disease and revascularization were not independent predictors of recurrence; thus, in this patient population, AF ablation should not be denied and can be performed together with CAD treatment.

Derivation of the Falls Decision Rule to exclude intracranial bleeding without head CT in older adults who have fallen

BACKGROUND

Ground-level falls are common among older adults and are the most frequent cause of traumatic intracranial bleeding. The aim of this study was to derive a clinical decision rule that safely excludes clinically important intracranial bleeding in older adults who present to the emergency department after a fall, without the need for a computed tomography (CT) scan of the head.

METHODS

This prospective cohort study in 11 emergency departments in Canada and the United States enrolled patients aged 65 years or older who presented after falling from standing on level ground, off a chair or toilet seat, or out of bed. We collected data on 17 potential predictor variables. The primary outcome was the diagnosis of clinically important intracranial bleeding within 42 days of the index emergency department visit. An independent adjudication committee, blinded to baseline data, determined the primary outcome. We derived a clinical decision rule using logistic regression.

RESULTS

The cohort included 4308 participants, with a median age of 83 years; 2770 (64%) were female, 1119 (26%) took anticoagulant medication and 1567 (36%) took antiplatelet medication. Of the participants, 139 (3.2%) received a diagnosis of clinically important intracranial bleeding. We developed a decision rule indicating that no head CT is required if there is no history of head injury on falling; no amnesia of the fall; no new abnormality on neurologic examination; and the Clinical Frailty Scale score is less than 5. Rule sensitivity was 98.6% (95% confidence interval [CI] 94.9%-99.6%), specificity was 20.3% (95% CI 19.1%-21.5%) and negative predictive value was 99.8% (95% CI 99.2%-99.9%).

INTERPRETATION

We derived a Falls Decision Rule, which requires external validation, followed by clinical impact assessment. Trial registration: ClinicalTrials. gov, no. NCT03745755.

Application and impact of Lasso regression in gastroenterology: A systematic review

Least absolute shrinkage and selection operator (Lasso) regression is a statistical technique that can be used to study the effects of clinical variables in outcome prediction. In this study, we aimed at systematically reviewing the application of Lasso regression in gastroenterology for developing predictive models and providing a method of performing Lasso regression. A comprehensive search strategy was conducted in PubMed, Embase and Cochrane CENTRAL databases (Keywords: lasso regression; gastrointestinal tract/diseases) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were screened for eligibility based on pre-defined selection criteria and the data was extracted using a standardized form. Total 16 studies were included, comprising a diverse range of gastroenterological disease-related outcomes. Sample sizes ranged from 134 to 8861 subjects. Eleven studies reported liver disease-related prediction models, while five focused on non-hepatic etiology models. Lasso regression was applied for variable selection, risk prediction and model development, with various validation methods and performance metrics used. Model performance metrics included Area Under the Receiver Operating Characteristics (AUROC), C-index and calibration plots. In gastroenterology, Lasso regression has been used in various diseases such as inflammatory bowel disease, liver disease and esophageal cancer. It is valuable for complex scenarios with many predictors. However, its effectiveness depends on high-quality and complete data. While it identifies important variables, it doesn't provide causal interpretations. Therefore, cautious interpretation is necessary considering the study design and data quality.

Predicting amyloid-beta pathology in the general population

INTRODUCTION

Reliable models to predict amyloid beta (Aβ) positivity in the general aging population are lacking but could become cost-efficient tools to identify individuals at risk of developing Alzheimer's disease.

METHODS

We developed Aβ prediction models in the clinical Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) Study (n = 4,119) including a broad range of easily ascertainable predictors (demographics, cognition and daily functioning, health and lifestyle factors). Importantly, we determined the generalizability of our models in the population-based Rotterdam Study (n = 500).

RESULTS

The best performing model in the A4 Study (area under the curve [AUC] = 0.73 [0.69-0.76]), including age, apolipoprotein E (APOE) ε4 genotype, family history of dementia, and subjective and objective measures of cognition, walking duration and sleep behavior, was validated in the independent Rotterdam Study with higher accuracy (AUC = 0.85 [0.81-0.89]). Yet, the improvement relative to a model including only age and APOE ε4 was marginal.

DISCUSSION

Aβ prediction models including inexpensive and non-invasive measures were successfully applied to a general population-derived sample more representative of typical older non-demented adults.

A 15-Gene-Based Risk Signature for Predicting Overall Survival in SCLC Patients Who Have Undergone Surgical Resection

Small cell lung cancer (SCLC) is a malignancy with a poor prognosis whose treatment has not progressed for decades. The survival benefit of surgery and the selection of surgical candidates are still controversial in SCLC. This study is the first report to identify transcriptomic alterations associated with prognosis and propose a gene expression-based risk signature that can be used to predict overall survival (OS) in SCLC patients who have undergone potentially curative surgery. An integrative transcriptome analysis of three gene expression datasets (GSE30219, GSE43346, and GSE149507) revealed 1734 up-regulated and 2907 down-regulated genes. Cox-Mantel test, Cox regression, and Lasso regression analyses were used to identify genes to be included in the risk signature. EGAD00001001244 and GSE60052-cohorts were used for internal and external validation, respectively. Overall survival was significantly poorer in patients with high-risk scores compared to the low-risk group. The discriminatory performance of the risk signature was superior to other parameters. Multivariate analysis showed that the risk signature has the potential to be an independent predictor of prognosis. The prognostic genes were enriched in pathways including regulation of transcription, cell cycle, cell metabolism, and angiogenesis. Determining the roles of the identified prognostic genes in the pathogenesis of SCLC may contribute to the development of new treatment strategies. The risk signature needs to be validated in a larger cohort of patients to test its usefulness in clinical decision-making.

Risk prediction model of self-reported hypertension for telemedicine based on the sociodemographic, occupational and health-related characteristics of seafarers: a cross-sectional epidemiological study

OBJECTIVES

High blood pressure is a common health concern among seafarers. However, due to the remote nature of their work, it can be difficult for them to access regular monitoring of their blood pressure. Therefore, the development of a risk prediction model for hypertension in seafarers is important for early detection and prevention. This study developed a risk prediction model of self-reported hypertension for telemedicine.

DESIGN

A cross-sectional epidemiological study was employed.

SETTING

This study was conducted among seafarers aboard ships. Data on sociodemographic, occupational and health-related characteristics were collected using anonymous, standardised questionnaires.

PARTICIPANTS

This study involved 8125 seafarers aged 18-70 aboard 400 vessels between November 2020 and December 2020. 4318 study subjects were included in the analysis. Seafarers over 18 years of age, active (on duty) during the study and willing to give informed consent were the inclusion criteria.

OUTCOME MEASURES

We calculated the adjusted OR (AOR) with 95% CIs using multiple logistic regression models to estimate the associations between sociodemographic, occupational and health-related characteristics and self-reported hypertension. We also developed a risk prediction model for self-reported hypertension for telemedicine based on seafarers' characteristics.

RESULTS

Among the 4318 participants, 55.3% and 44.7% were non-officers and officers, respectively. 20.8% (900) of the participants reported having hypertension. Multivariable analysis showed that age (AOR: 1.08, 95% CI 1.07 to 1.10), working long hours per week (AOR: 1.02, 95% CI 1.01 to 1.03), work experience at sea (10+ years) (AOR: 1.79, 95% CI 1.33 to 2.42), being a non-officer (AOR: 1.75, 95% CI 1.44 to 2.13), snoring (AOR: 3.58, 95% CI 2.96 to 4.34) and other health-related variables were independent predictors of self-reported hypertension, which were included in the final risk prediction model. The sensitivity, specificity and accuracy of the predictive model were 56.4%, 94.4% and 86.5%, respectively.

CONCLUSION

A risk prediction model developed in the present study is accurate in predicting self-reported hypertension in seafarers' onboard ships.

High-Throughput CSF Proteomics and Machine Learning to Identify Proteomic Signatures for Parkinson Disease Development and Progression

BACKGROUND AND OBJECTIVES

This study aimed to identify CSF proteomic signatures characteristic of Parkinson disease (PD) and evaluate their clinical utility.

METHODS

This observational study used data from the Parkinson's Progression Markers Initiative (PPMI), which enrolled patients with PD, healthy controls (HCs), and non-PD participants carrying GBA1, LRRK2, and/or SNCA pathogenic variants (genetic prodromals) at international sites. Study participants were chosen from PPMI enrollees based on the availability of aptamer-based CSF proteomic data, quantifying 4,071 proteins, and classified as patients with PD without GBA1, LRRK2, and/or SNCA pathogenic variants (nongenetic PD), HCs, patients with PD carrying the aforementioned pathogenic variants (genetic PD), or genetic prodromals. Differentially expressed protein (DEP) analysis and the least absolute shrinkage and selection operator (LASSO) were applied to the data from nongenetic PD and HCs. Signatures characteristics of nongenetic PD were quantified as a PD proteomic score (PD-ProS), validated internally and then externally using data of 1,556 CSF proteins from the LRRK2 Cohort Consortium (LCC). We further tested the PD-ProS in genetic PD and genetic prodromals and examined associations with clinical progression.

RESULTS

Data from 279 patients with nongenetic PD (mean ± SD, age 62.0 ± 9.6 years; male 67.7%) and 141 HCs (age 60.5 ± 11.9 years; male 64.5%) were used for PD-ProS derivation. From 23 DEPs, LASSO determined weights of 14 DEPs for the PD-ProS (area under the curve [AUC] 0.83, 95% CI 0.78-0.87), validated in an independent internal validation cohort of 71 patients with nongenetic PD and 35 HCs (AUC 0.81, 95% CI 0.73-0.90). In the LCC, only 5 of the 14 DEPs were also measured. Notably, these 5 DEPs still distinguished 34 patients with nongenetic PD from 31 HCs with the same weights (AUC 0.75, 95% CI 0.63-0.87). Furthermore, the PD-ProS distinguished 258 patients with genetic PD from 365 genetic prodromals. Finally, regardless of genetic status, the PD-ProS independently predicted both cognitive and motor decline in PD (dementia, adjusted hazard ratio in the highest quintile [aHR-Q5] 2.8 [95% CI 1.6-5.0]; Hoehn and Yahr stage IV, aHR-Q5 2.1 [95% CI 1.1-4.0]).

DISCUSSION

By integrating high-throughput proteomics with machine learning, we identified PD-associated CSF proteomic signatures crucial for PD development and progression.

TRIAL REGISTRATION INFORMATION

ClinicalTrials.gov (NCT01176565). A link to the trial registry page is clinicaltrials.gov/ct2/show/NCT01141023.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that the CSF proteome contains clinically important information regarding the development and progression of Parkinson disease that can be deciphered by a combination of high-throughput proteomics and machine learning.

Atrial Abnormalities in Brugada Syndrome: Evaluation With ECG Imaging

BACKGROUND

Patients with Brugada syndrome (BrS) have an increased risk of arrhythmias, including atrial tachyarrhythmias (ATas).

OBJECTIVES

The purpose of this study was to assess underlying atrial cardiomyopathy in BrS and the effect of ajmaline (AJM) test on the atrium of BrS patients using electrocardiogram imaging (ECGI).

METHODS

All consecutive patients diagnosed with BrS in a monocentric registry were screened and included if they met the following criteria: 1) BrS diagnosed following current recommendations; and 2) ECGI map performed before and after AJM with a standard protocol. Consecutive patients with no structural heart disease or BrS who had undergone ECGI were included as a control group. Genetic analysis for SCN5A was performed in all BrS patients. Total atrial conduction time (TACT) and local atrial conduction time (LACT) were calculated from atrial ECGI. The primary endpoint was ATas during follow-up.

RESULTS

Forty-three consecutive BrS patients and 40 control patients were included. Both TACT and LACT were significantly prolonged in BrS patients compared with control patients. Furthermore, TACT and LACT were significantly higher after AJM administration and in BrS patients who were carriers of a pathogenic/likely pathogenic SCN5A variant. After a mean follow-up of 40.9 months, 6 patients experienced a first ATa occurrence (all in the BrS group, 13.9%). TACT was the only independent predictor of ATas with a cutoff of >138.5 ms (sensitivity 0.92 [95% CI: 0.83-0.98], specificity 0.70 [95% CI: 0.59-0.81]).

CONCLUSIONS

ECGI-calculated TACT and LACT are significantly prolonged in BrS patients compared with control patients, and in BrS patients after AJM. This may be consistent with a concealed atrial cardiomyopathy in BrS.

Pay attention: you can fall! The Mini-BESTest scale and the turning duration of the TUG test provide valid balance measures in neurological patients: a prospective study with falls as the balance criterion

Background

Balance, i.e., the ability not to fall, is often poor in neurological patients and this impairment increases their risk of falling. The Mini-Balance Evaluation System Test (Mini-BESTest), a rating scale, the Timed Up and Go (TUG) test, and gait measures are commonly used to quantify balance. This study assesses the criterion validity of these measures as balance measures.

Methods

The probability of being a faller within nine months was used as the balance criterion. The Mini-BESTest, TUG (instrumented with inertial sensors), and walking test were administered before and after inpatient rehabilitation. Multiple and LASSO logistic regressions were used for the analysis. The diagnostic accuracy of the model was assessed with the area under the curve (AUC) of the receiver operating characteristic curve. Mobility measure validity was compared with the Akaike Information Criterion (AIC).

Results

Two hundred and fourteen neurological patients (stroke, peripheral neuropathy, or parkinsonism) were recruited. In total, 82 patients fell at least once in the nine-month follow-up. The Mini-BESTest (AUC = 0.69; 95%CI: 0.62-0.76), the duration of the TUG turning phase (AUC = 0.69; 0.62-0.76), and other TUG measures were significant faller predictors in regression models. However, only the turning duration (AIC = 274.0) and Mini-BESTest (AIC = 276.1) substantially improved the prediction of a baseline model, which only included fall risk factors from the medical history (AIC = 281.7). The LASSO procedure selected gender, disease chronicity, urinary incontinence, the Mini-BESTest, and turning duration as optimal faller predictors.

Conclusion

The TUG turning duration and the Mini-BESTest predict the chance of being a faller. Their criterion validity as balance measures in neurological patients is substantial.

Risk factors for chronic ankle instability after first episode of lateral ankle sprain: A retrospective analysis of 362 cases

BACKGROUND

Chronic ankle instability (CAI) is a common sequela following an acute lateral ankle sprain (LAS). To treat an acute LAS more effectively and efficiently, it is important to identify patients at substantial risk for developing CAI. This study identifies magnetic resonance imaging (MRI) manifestations for predicting CAI development after a first episode of LAS and explores appropriate clinical indications for ordering MRI scans for these patients.

METHODS

All patients with a first-episode LAS who received plain radiograph and MRI scanning within the first 2 weeks after LAS from December 1, 2017 to December 1, 2019 were identified. Data were collected using the Cumberland Ankle Instability Tool at final follow-up. Demographic and other related clinical variables, including age, sex, body mass index, and treatment were also recorded. Univariable and multivariable analyses were performed successively to identify risk factors for CAI after first-episode LAS.

RESULTS

A total 131 out of 362 patients with a mean follow-up of 3.0 ± 0.6 years (mean ± SD; 2.0-4.1 years) developed CAI after first-episode LAS. According to multivariable regression, development of CAI after first-episode LAS was associated with 5 prognostic factors: age (odds ratio (OR) = 0.96, 95% confidence interval (95%CI): 0.93-1.00, p = 0.032); body mass index (OR = 1.09, 95%CI: 1.02-1.17, p = 0.009); posterior talofibular ligament injury (OR = 2.17, 95%CI: 1.05-4.48, p = 0.035); large bone marrow lesion of the talus (OR = 2.69, 95%CI: 1.30-5.58, p = 0.008), and Grade 2 effusion of the tibiotalar joint (OR = 2.61, 95%CI: 1.39-4.89, p = 0.003). When patients had at least 1 positive clinical finding in the 10-m walk test, anterior drawer test, or inversion tilt test, they had a 90.2% sensitivity and 77.4% specificity in terms of detecting at least 1 prognostic factor by MRI.

CONCLUSION

MRI scanning is valuable in predicting CAI after first-episode LAS for those patients with at least 1 positive clinical finding in the 10-m walk test, anterior drawer test, and inversion tilt test. Further prospective and large-scale studies are necessary for validation.

Risk-prediction models for intravenous immunoglobulin resistance in Kawasaki disease: Risk-of-Bias Assessment using PROBAST

BACKGROUND

The prediction model of intravenous immunoglobulin (IVIG) resistance in Kawasaki disease can calculate the probability of IVIG resistance and provide a basis for clinical decision-making. We aim to assess the quality of these models developed in the children with Kawasaki disease.

METHODS

Studies of prediction models for IVIG-resistant Kawasaki disease were identified through searches in the PubMed, Web of Science, and Embase databases. Two investigators independently performed literature screening, data extraction, quality evaluation, and discrepancies were settled by a statistician. The checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS) was used for data extraction, and the prediction models were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST).

RESULTS

Seventeen studies meeting the selection criteria were included in the qualitative analysis. The top three predictors were neutrophil measurements (peripheral neutrophil count and neutrophil %), serum albumin level, and C-reactive protein (CRP) level. The reported area under the curve (AUC) values for the developed models ranged from 0.672 (95% confidence interval [CI]: 0.631-0.712) to 0.891 (95% CI: 0.837-0.945); The studies showed a high risk of bias (ROB) for modeling techniques, yielding a high overall ROB.

CONCLUSION

IVIG resistance models for Kawasaki disease showed high ROB. An emphasis on improving their quality can provide high-quality evidence for clinical practice.

IMPACT STATEMENT

This study systematically evaluated the risk of bias (ROB) of existing prediction models for intravenous immunoglobulin (IVIG) resistance in Kawasaki disease to provide guidance for future model development meeting clinical expectations. This is the first study to systematically evaluate the ROB of IVIG resistance in Kawasaki disease by using PROBAST. ROB may reduce model performance in different populations. Future prediction models should account for this problem, and PROBAST can help improve the methodological quality and applicability of prediction model development.

Association between adjuvant radiation treatment and breast cancer-specific mortality among older women with comorbidity burden: A comparative effectiveness analysis of SEER-MHOS

BACKGROUND

The National Comprehensive Cancer Network suggested that older women with low-risk breast cancer (LRBC; i.e., early-stage, node-negative, and estrogen receptor-positive) could omit adjuvant radiation treatment (RT) after breast-conserving surgery (BCS) if they were treated with hormone therapy. However, the association between RT omission and breast cancer-specific mortality among older women with comorbidity is not fully known.

METHODS

1105 older women (≥65 years) with LRBC in 1998-2012 were queried from the Surveillance, Epidemiology, and End Results-Medicare Health Outcomes Survey data resource and were followed up through July 2018. Latent class analysis was performed to identify comorbidity burden classes. A propensity score-based inverse probability of treatment weighting (IPTW) was applied to Cox regression models to obtain subdistribution hazard ratios (HRs) and 95% CI for cancer-specific mortality considering other causes of death as competing risks, overall and separately by comorbidity burden class.

RESULTS

Three comorbidity burden (low, moderate, and high) groups were identified. A total of 318 deaths (47 cancer-related) occurred. The IPTW-adjusted Cox regression analysis showed that RT omission was not associated with short-term, 5- and 10-year cancer-specific death (p = 0.202 and p = 0.536, respectively), regardless of comorbidity burden. However, RT omission could increase the risk of long-term cancer-specific death in women with low comorbidity burden (HR = 1.98, 95% CI = 1.17, 3.33), which warrants further study.

CONCLUSIONS

Omission of RT after BCS is not associated with an increased risk of cancer-specific death and is deemed a reasonable treatment option for older women with moderate to high comorbidity burden.

Role of fatty liver in the epidemic of advanced chronic liver disease among people with HIV: protocol for the Canadian LIVEHIV multicentre prospective cohort

INTRODUCTION

Advanced chronic liver disease (ACLD) is a major cause of death for people with HIV (PWH). While viral hepatitis coinfections are largely responsible for this trend, metabolic dysfunction-associated steatotic liver disease (MASLD) is an emerging concern for PWH. We aimed to assess the contribution of MASLD to incident ACLD in PWH.

METHODS AND ANALYSIS

This multicentre prospective observational cohort study will enrol 968 consecutive HIV monoinfected patients from four Canadian sites, excluding subjects with alcohol abuse, liver disease other than MASLD, or ACLD at baseline. Participants will be followed annually for 4 years by clinical evaluation, questionnaires, laboratory testing and Fibroscan to measure liver stiffness measurement (LSM) and controlled attenuation parameter (CAP). The primary outcome will be incidence of ACLD, defined as LSM>10 kPa, by MASLD status, defined as CAP≥285 dB/m with at least one metabolic abnormality, and to develop a score to classify PWH according to their risk of ACLD. Secondary outcomes will include health-related quality of life (HRQoL) and healthcare resource usage. Kaplan-Meier survival method and Cox proportional hazards regression will calculate the incidence and predictors of ACLD, respectively. Propensity score methods and marginal structural models will account for time-varying exposures. We will split the cohort into a training set (to develop the risk score) and a validation set (for validation of the score). HRQoL scores and healthcare resource usage will be compared by MASLD status using generalised linear mixed effects model.

ETHICS AND DISSEMINATION

This protocol has been approved by the ethics committees of all participating institutions. Written informed consent will be obtained from all study participants. The results of this study will be shared through scientific publications and public presentations to advocate for the inclusion of PWH in clinical trials of MASLD-targeted therapies and case-finding of ACLD in PWH.

Predicting deep surgical site infection in patients receiving open posterior instrumented thoracolumbar surgery: A-DOUBLE-SSI risk score - a large retrospective multicenter cohort study in China

BACKGROUND

To develop a practical prediction model to predict the risk of deep surgical site infection (SSI) in patients receiving open posterior instrumented thoracolumbar surgery.

METHODS

Data of 3419 patients in four hospitals from 1 January 2012 to 30 December 2021 were evaluated. The authors used clinical knowledge-driven, data-driven, and decision tree model to identify predictive variables of deep SSI. Forty-three candidate variables were collected, including 5 demographics, 29 preoperative, 5 intraoperative, and 4 postoperative variables. According to model performance and clinical practicability, the best model was chosen to develop a risk score. Internal validation was performed by using bootstrapping methods.

RESULTS

After open posterior instrumented thoracolumbar surgery, 158 patients (4.6%) developed deep SSI. The clinical knowledge-driven model yielded 12 predictors of deep SSI, while the data-driven and decision tree model produced 11 and 6 predictors, respectively. A knowledge-driven model, which had the best C-statistics [0.81 (95% CI: 0.78-0.85)] and superior calibration, was chosen due to its favorable model performance and clinical practicality. Moreover, 12 variables were identified in the clinical knowledge-driven model, including age, BMI, diabetes, steroid use, albumin, duration of operation, blood loss, instrumented segments, powdered vancomycin administration, duration of drainage, postoperative cerebrospinal fluid leakage, and early postoperative activities. In bootstrap internal validation, the knowledge-driven model still showed optimal C-statistics (0.79, 95% CI: 0.75-0.83) and calibration. Based on these identified predictors, a risk score for deep SSI incidence was created: the A-DOUBLE-SSI (Age, D [Diabetes, Drainage], O [duration of Operation, vancOmycin], albUmin, B [BMI, Blood loss], cerebrospinal fluid Leakage, Early activities, Steroid use, and Segmental Instrumentation) risk score. Based on the A-DOUBLE-SSI score system, the incidence of deep SSI increased in a graded fashion from 1.06% (A-DOUBLE-SSIs score ≤8) to 40.6% (A-DOUBLE-SSIs score>15).

CONCLUSIONS

The authors developed a novel and practical model, the A-DOUBLE-SSIs risk score, that integrated easily accessible demographics, preoperative, intraoperative, and postoperative variables and could be used to predict individual risk of deep SSI in patients receiving open posterior instrumented thoracolumbar surgery.

Predicting Fear Extinction in Posttraumatic Stress Disorder

Fear extinction is the basis of exposure therapies for posttraumatic stress disorder (PTSD), but half of patients do not improve. Predicting fear extinction in individuals with PTSD may inform personalized exposure therapy development. The participants were 125 trauma-exposed adults (96 female) with a range of PTSD symptoms. Electromyography, electrocardiogram, and skin conductance were recorded at baseline, during dark-enhanced startle, and during fear conditioning and extinction. Using a cross-validated, hold-out sample prediction approach, three penalized regressions and conventional ordinary least squares were trained to predict fear-potentiated startle during extinction using 50 predictor variables (5 clinical, 24 self-reported, and 21 physiological). The predictors, selected by penalized regression algorithms, were included in multivariable regression analyses, while univariate regressions assessed individual predictors. All the penalized regressions outperformed OLS in prediction accuracy and generalizability, as indexed by the lower mean squared error in the training and holdout subsamples. During early extinction, the consistent predictors across all the modeling approaches included dark-enhanced startle, the depersonalization and derealization subscale of the dissociative experiences scale, and the PTSD hyperarousal symptom score. These findings offer novel insights into the modeling approaches and patient characteristics that may reliably predict fear extinction in PTSD. Penalized regression shows promise for identifying symptom-related variables to enhance the predictive modeling accuracy in clinical research.

Contraceptive use pattern based on the number and composition of children among married women in sub-Saharan Africa: a multilevel analysis

BACKGROUND

The relationship between composition of children and contraception use has received limited scholarly attention in sub-Saharan Africa. In this study, we examined the relationship between contraceptive methods, the number and composition of children in SSA.

METHODS

Data on 21 countries in sub-Saharan Africa (SSA) countries that had a Demographic and Health Survey on or before 2015 were analysed. We applied a multilevel multinomial logistic regression model to assess the influence of family composition on contraceptive use. Adjusted relative risk ratio (aRRR) and 95% CI were estimated. The significant level was set at p < 0.05. All the analyses were conducted using weighted data.

RESULTS

Women who had one son and two daughters (aRRR = 0.85, CI = 0.75, 0.95), two sons and one daughter (aRRR = 0.81 CI = 0.72, 0.92), one son and three daughters (aRRR = 0.66, CI = 0.54, 0.80), two sons and two daughters (aRRR = 0.59, CI = 0.50, 0.69), and three or more sons (aRRR = 0.75, CI = 0.63, 0.91) were less likely to use temporary modern contraceptive methods. Those with two sons and two daughters were less likely to use traditional methods (aRRR = 0.52, CI = 0.35, 0.78). Women in the older age group (35-49 years) were less likely to use temporary modern methods (aRRR = 0.60; 95%CI; 0.57, 0.63). However, this group of women were more likely to use permanent (sterilization) (aRRR = 1.71; 95%CI; 1.50, 1.91) and traditional methods (aRRR = 1.28; 95%CI; 1.14, 1.43).

CONCLUSION

These findings suggest that contraception needs of women vary based on the composition of their children, hence a common approach or intervention will not fit. As a result, contraception interventions ought to be streamlined to meet the needs of different categories of women. The findings can inform policymakers and public health professionals in developing effective strategies to improve contraceptive use in SSA.

Patients with Clinically Suspected Gallstone Disease: A More Selective Ultrasound May Improve Treatment Related Outcomes

This study aimed to quantify the confirmation of gallstones on ultrasound (US) in patients with suspicion of gallstone disease. To aid general practitioners (GPs) in diagnostic workup, a model to predict gallstones was developed. A prospective cohort study was conducted in two Dutch general hospitals. Patients (≥18 years) were eligible for inclusion when referred by GPs for US with suspicion of gallstones. The primary outcome was the confirmation of gallstones on US. A multivariable regression model was developed to predict the presence of gallstones. In total, 177 patients were referred with a clinical suspicion of gallstones. Gallstones were found in 64 of 177 patients (36.2%). Patients with gallstones reported higher pain scores (VAS 8.0 vs. 6.0, p < 0.001), less frequent pain (21.9% vs. 54.9%, p < 0.001), and more often met criteria for biliary colic (62.5% vs. 44.2%, p = 0.023). Predictors for the presence of gallstones were a higher pain score, frequency of pain less than weekly, biliary colic, and an absence of heartburn. The model showed good discrimination between patients with and without gallstones (C-statistic 0.73, range: 0.68-0.76). Clinical diagnosis of symptomatic gallstone disease is challenging. The model developed in this study may aid in the selection of patients for referral and improve treatment related outcomes.

Low C-reactive Protein and Urea Distinguish Primary Nonfunction From Early Allograft Dysfunction Within 48 Hours of Liver Transplantation

Primary nonfunction (PNF) is a life-threatening complication of liver transplantation (LT), but in the early postoperative period, it can be difficult to differentiate from early allograft dysfunction (EAD). The aim of this study was to determine if serum biomarkers can distinguish PNF from EAD in the initial 48 h following LT.

Materials and Methods

A retrospective study of adult patients that underwent LT between January 2010 and April 2020 was performed. Clinical parameters, absolute values and trends of C-reactive protein (CRP), blood urea, creatinine, liver function tests, platelets, and international normalized ratio in the initial 48 h after LT were compared between the EAD and PNF groups.

Results

There were 1937 eligible LTs, with PNF and EAD occurring in 38 (2%) and 503 (26%) patients, respectively. A low serum CRP and urea were associated with PNF. CRP was able to differentiate between the PNF and EAD on postoperative day (POD)1 (20 versus 43 mg/L; P < 0.001) and POD2 (24 versus 77; P < 0.001). The area under the receiver operating characteristic curve (AUROC) of POD2 CRP was 0.770 (95% confidence interval [CI] 0.645-0.895). The urea value on POD2 (5.05 versus 9.0 mmol/L; P = 0.002) and trend of POD2:1 ratio (0.71 versus 1.32 mmol/L; P < 0.001) were significantly different between the groups. The AUROC of the change in urea from POD1 to 2 was 0.765 (95% CI 0.645-0.885). Aspartate transaminase was significantly different between the groups, with an AUROC of 0.884 (95% CI 0.753-1.00) on POD2.

Discussion

The biochemical profile immediately following LT can distinguish PNF from EAD; CRP, urea, and aspartate transaminase are more effective than ALT and bilirubin in distinguishing PNF from EAD in the initial postoperative 48 h. Clinicians should consider the values of these markers when making treatment decisions.

Predicting 30-day readmission following total knee arthroplasty using machine learning and clinical expertise applied to clinical administrative and research registry data in an Australian cohort

BACKGROUND

Thirty-day readmission is an increasingly important problem for total knee arthroplasty (TKA) patients. The aim of this study was to develop a risk prediction model using machine learning and clinical insight for 30-day readmission in primary TKA patients.

METHOD

Data used to train and internally validate a multivariable predictive model were obtained from a single tertiary referral centre for TKA located in Victoria, Australia. Hospital administrative data and clinical registry data were utilised, and predictors were selected through systematic review and subsequent consultation with clinicians caring for TKA patients. Logistic regression and random forest models were compared to one another. Calibration was evaluated by visual inspection of calibration curves and calculation of the integrated calibration index (ICI). Discriminative performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC).

RESULTS

The models developed in this study demonstrated adequate calibration for use in the clinical setting, despite having poor discriminative performance. The best-calibrated readmission prediction model was a logistic regression model trained on administrative data using risk factors identified from systematic review and meta-analysis, which are available at the initial consultation (ICI = 0.012, AUC-ROC = 0.589). Models developed to predict complications associated with readmission also had reasonable calibration (ICI = 0.012, AUC-ROC = 0.658).

CONCLUSION

Discriminative performance of the prediction models was poor, although machine learning provided a slight improvement. The models were reasonably well calibrated, meaning they provide accurate patient-specific probabilities of these outcomes. This information can be used in shared clinical decision-making for discharge planning and post-discharge follow up.

A diagnostic prediction model for hypertension in Han and Yugur population from the China National Health Survey (CNHS)

BACKGROUND

The prevalence of hypertension is high among Chinese adults, thus, identifying non-hypertensive individuals at high risk for intervention will help to improve the efficiency of primary prevention strategies.

METHODS

The cross-sectional data on 9699 participants aged 20 to 80 years were collected from the China National Health Survey in Gansu and Hebei provinces in 2016 to 2017, and they were nonrandomly split into the training set and validation set based on location. Multivariable logistic regression analysis was performed to develop the diagnostic prediction model, which was presented as a nomogram and a website with risk classification. Predictive performances of the model were evaluated using discrimination and calibration, and were further compared with a previously published model. Decision curve analysis was used to calculate the standardized net benefit for assessing the clinical usefulness of the model.

RESULTS

The Lasso regression analysis identified the significant predictors of hypertension in the training set, and a diagnostic model was developed using logistic regression. A nomogram with risk classification was constructed to visualize the model, and a website ( https://chris-yu.shinyapps.io/hypertension_risk_prediction/ ) was developed to calculate the exact probabilities of hypertension. The model showed good discrimination and calibration, with the C-index of 0.789 (95% confidence interval [CI]: 0.768, 0.810) through internal validation and 0.829 (95% CI: 0.816, 0.842) through external validation. Decision curve analysis demonstrated that the model was clinically useful. The model had a higher area under receiver operating characteristic curves in training and validation sets compared with a previously published diagnostic model based on Northern China population.

CONCLUSION

This study developed and validated a diagnostic model for hypertension prediction in Gansu Province. A nomogram and a website were developed to make the model conveniently used to facilitate the individualized prediction of hypertension in the general population of Han and Yugur.

A Novel Risk Assessment Model Predicts Major Bleeding Risk at Admission in Medical Inpatients

Background

Venous thromboembolism (VTE) is the leading cause of preventable hospital death in the US. Guidelines from the American College of Chest Physicians and American Society for Hematology recommend providing pharmacological VTE prophylaxis to acutely or critically ill medical patients at acceptable bleeding risk, but there is currently only one validated risk assessment model (RAM) for estimating bleeding risk. We developed a RAM using risk factors at admission and compared it with the International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) model.

Methods

A total of 46,314 medical patients admitted to a Cleveland Clinic Health System hospital from 2017-2020 were included. Data were split into training (70%) and validation (30%) sets with equivalent bleeding event rates in each set. Potential risk factors for major bleeding were identified from the IMPROVE model and literature review. Penalized logistic regression using LASSO was performed on the training set to select and regularize important risk factors for the final model. The validation set was used to assess model calibration and discrimination and compare performance with IMPROVE. Bleeding events and risk factors were confirmed through chart review.

Results

The incidence of major in-hospital bleeding was 0.58%. Active peptic ulcer (OR = 5.90), prior bleeding (OR = 4.24), and history of sepsis (OR = 3.29) were the strongest independent risk factors. Other risk factors included age, male sex, decreased platelet count, increased INR, increased PTT, decreased GFR, ICU admission, CVC or PICC placement, active cancer, coagulopathy, and in-hospital antiplatelet drug, steroid, or SSRI use. In the validation set, the Cleveland Clinic Bleeding Model (CCBM) had better discrimination than IMPROVE (0.86 vs. 0.72, p < .001) and, at equivalent sensitivity (54%), categorized fewer patients as high-risk (6.8% vs. 12.1%, p < .001).

Conclusions

From a large population of medical inpatients, we developed and validated a RAM to accurately predict bleeding risk at admission. The CCBM may be used in conjunction with VTE risk calculators to decide between mechanical and pharmacological prophylaxis for at-risk patients.