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

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.

Serum testosterone mirrors inflammation parameters in females hospitalized with COVID-19

BACKGROUND

While low testosterone (T) was described as a predictor of unfavorable coronavirus-disease 19 (COVID-19) outcome in men, data concerning the role of T in women with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are scant and limited to small cohorts. This study investigated the relationship between serum T values and outcomes of COVID-19 in a large female hospitalized cohort.

METHODS

One-hundred-sixty-eight adult women (median age 77, range 18-100 years; 154 in post-menopause) hospitalized for COVID-19 were assessed for PaO2/Fio2 ratio, serum T and inflammatory parameters.

RESULTS

Median duration for hospital stay was 14.2 days (range 1-115) with overall mortality of 26% (n = 44). Subjects who died were significantly older (p < 0.001), had significantly more comorbidities (p = 0.015) and higher serum T (p = 0.040), white blood cells (p = 0.007), c-reactive protein (CRP; p < 0.001), interleukin-6 (IL-6; p < 0.001), procalcitonin (PCT; p < 0.001), lactate dehydrogenase (LDH; p = 0.001), D-dimer (p = 0.035), fibrinogen (p = 0.038) and lower serum free-triiodothyronine (FT3; p < 0.001) and luteinizing hormone (LH; p = 0.024) values. In post-menopausal women, significant associations were observed between T levels and serum CRP (rho: 0.23; p = 0.002), IL-6 (rho: 0.41; p < 0.001), LDH (rho: 0.34; p < 0.001), D-Dimer (rho: 0.21; p = 0.008), PCT (rho: 0.26; p = 0.001) and HDL cholesterol (rho:  - 0,22, p = 0.008). In multivariate regression analyses, serum T maintained the significant association with mortality after correction for age, coexistent comorbidities and serum LH and FT3, whereas it was lost after correction for inflammatory parameters.

CONCLUSION

In females, high serum T levels might be a mirror of inflammatory phenotype and worse COVID-19 course.

Macrophage morphology and distribution are strong predictors of prognosis in resected colorectal liver metastases: results from an external retrospective observational study

INTRODUCTION

Tumor-associated macrophages (TAMs) are key components of a tumoral microenvironment and have been shown to impact prognosis in different cancers. Previously reported data showed that TAM morphology correlates with prognosis in colorectal liver metastases (CLMs) after hepatectomy, with smaller TAMs (S-TAMs) conferring a more favorable prognosis than larger ones (L-TAMs). This study aims to externally validate this finding.

MATERIAL AND METHODS

The external cohort consisted of 84 formalin-fixed and paraffin-embedded surgical samples of CLMs and peritumoral tissue. Two-micrometer-section slides were obtained; the area and perimeter of 21 macrophages in each slide were recorded. The endpoints were TAMs morphometrics and their prognostic significance in relation to disease-free survival (DFS).

RESULTS

The average macrophage perimeter was 71.5±14.1 μm whilst the average area was 217.7±67.8 μm 2 . At univariate analysis, the TAM area demonstrated a statistically significant association with DFS ( P =0.0006). Optimal area cutoff value was obtained, showing a sensitivity and specificity of 92 and 56%, respectively. S-TAMs and L-TAMs were associated with 3-year DFS rates of 60 and 8.5%, respectively ( P <0.001). Multivariate analysis confirmed the predictive role of TAM area for DFS [hazard ratio (HR)=5.03; 95% CI=1.70-14.94; P =0.003]. Moreover, in a subset of patients ( n =12) characterized by unfavorable ( n =6, recurrence within 3 months) or favorable ( n =6, no recurrence after 48 months) prognosis, TAMs showed a different distribution: L-TAMs were more abundant and closer to the tumor invasive margin in patients that encountered early recurrence and tended to cluster in foci significantly larger ( P =0.02).

CONCLUSIONS

This external validation confirms that morphometric characterization of TAMs can serve as a simple readout of their diversity and allows to reliably stratify patient outcomes and predict disease recurrence after hepatectomy for CLMs.

Performance of a prediabetes risk prediction model: A systematic review

Backgrounds

The prediabetes population is large and easily overlooked because of the lack of obvious symptoms, which can progress to diabetes. Early screening and targeted interventions can substantially reduce the rate of conversion of prediabetes to diabetes. Therefore, this study systematically reviewed prediabetes risk prediction models, performed a summary and quality evaluation, and aimed to recommend the optimal model.

Methods

We systematically searched five databases (Cochrane, PubMed, Embase, Web Of Science, and CNKI) for published literature related to prediabetes risk prediction models and excluded preprints, duplicate publications, reviews, editorials, and other studies, with a search time frame of March 01, 2023. Data were categorized and summarized using a standardized data extraction form that extracted data including author; publication date; study design; country; demographic characteristics; assessment tool name; sample size; study type; and model-related indicators. The PROBAST tool was used to assess the risk of bias profile of included studies.

Findings

14 studies with a total of 15 models were eventually included in the systematic review. We found that the most common predictors of models were age, family history of diabetes, gender, history of hypertension, and BMI. Most of the studies (83.3%) had a high risk of bias, mainly related to under-reporting of outcome information and poor methodological design during the development and validation of models. Due to the low quality of included studies, the evidence for predictive validity of the available models is unclear.

Interpretation

We should pay attention to the early screening of prediabetes patients and give timely pharmacological and lifestyle interventions. The predictive performance of the existing model is not satisfactory, and the model building process can be standardized and external validation can be added to improve the accuracy of the model in the future.

Inhalational versus Intravenous General Anesthesia for mechanical thrombectomy for stroke: A single centre retrospective study

BACKGROUND

When general anesthesia is used for endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), the choice of anesthetic agents for maintenance remains inconclusive. The different effects of intravenous anesthetic and volatiles agents on cerebral hemodynamics are known and may explain differences in outcomes of patients with cerebral pathologies exposed to the different anesthetic modalities. In this single institutional retrospective study, we assessed the impact of total intravenous (TIVA) and inhalational anesthesia on outcomes after EVT.

METHODS

We conducted a retrospective analysis of all patients ≥ 18 years who underwent EVT for AIS of the anterior or posterior circulation under general anesthesia. Baseline patient characteristics, anesthetic agents, intra operative hemodynamics, stroke characteristics, time intervals and clinical outcome data were collected and analyzed.

RESULTS

The study cohort consisted of 191 patients. After excluding 76 patients who were lost to follow up at 90 days, 51 patients received inhalational anesthesia and 64 patients who received TIVA were analyzed. The clinical characteristics between the groups were comparable. Multivariate logistic regression analysis of outcome measures for TIVA versus inhalational anesthesia showed significantly increased odds of good functional outcome (mRS 0-2) at 90 days (adjusted odds ratio, 3.24; 95% CI, 1.25-8.36; p = 0.015) and a non-significant trend towards decreased mortality (adjusted odds ratio, 0.73; CI, 0.15-3.6; p = 0.70).

CONCLUSION

Patients who had TIVA for mechanical thrombectomy had significantly increased odds of good functional outcome at 90 days and a non-significant trend towards decrease in mortality. These findings warrant further investigation with large randomized, prospective trials.

Development of a Nomogram to Estimate the 60-Day Probability of Death or Culling Due to Severe Clinical Mastitis in Dairy Cows at First Veterinary Clinical Evaluation

Severe clinical mastitis is a frequent disease of dairy cattle. An effective mean of predicting survival despite treatment would be helpful for making euthanasia decisions in poor prognosis cases. The objective was to develop a nomogram for prediction of death or culling in the 60 days following a severe mastitis episode in dairy cows at first veterinary visit in farm settings. A total of 224 dairy cows presenting severe clinical mastitis and examined for the first time by a veterinarian were included in a prospective study. Clinical and laboratory (complete blood cell count, L-lactate, cardiac troponin I, milk culture) variables were recorded. Animals were followed for 60 days. A nomogram was built with an adaptive elastic-net Cox proportional hazards model. Performances and relevance were evaluated by area under the receiver operating characteristic curve (AUC), Harrell’s concordance index (C-index), calibration curve, decision curve analysis (DCA) and misclassification cost term (MCT). The nomogram included: lactation number, recumbency, depression intensity, capillary refilling time, ruminal motility rate, dehydration level, lactates concentration, hematocrit, band neutrophils count, monocyte count, and milk bacteriology. The AUC and C-index showed a good calibration and ability to discriminate. The DCA suggested that the nomogram was clinically relevant. Euthanizing animals having less than 25% probability of survival is economically optimal. It could be used for early euthanasia decisions in animals that would not survive despite treatment. To facilitate the use of this nomogram by veterinarians, a web-based app was developed.

Development and external validation of prediction models for critical outcomes of unvaccinated COVID-19 patients based on demographics, medical conditions and dental status

Background

Multiple prediction models were developed for critical outcomes of COVID-19. However, prediction models using predictors which can be easily obtained in clinical practice and on dental status are scarce.

Aim

The study aimed to develop and externally validate prediction models for critical outcomes of COVID-19 for unvaccinated adult patients in hospital settings based on demographics, medical conditions, and dental status.

Methods

A total of 285 and 352 patients from two hospitals in the Netherlands were retrospectively included as derivation and validation cohorts. Demographics, medical conditions, and dental status were considered potential predictors. The critical outcomes (death and ICU admission) were considered endpoints. Logistic regression analyses were used to develop two models: for death alone and for critical outcomes. The performance and clinical values of the models were determined in both cohorts.

Results

Age, number of teeth, chronic kidney disease, hypertension, diabetes, and chronic obstructive pulmonary diseases were the significant independent predictors. The models showed good to excellent calibration with observed: expected (O:E) ratios of 0.98 (95%CI: 0.76 to 1.25) and 1.00 (95%CI: 0.80 to 1.24), and discrimination with shrunken area under the curve (AUC) values of 0.85 and 0.79, based on the derivation cohort. In the validation cohort, the models showed good to excellent discrimination with AUC values of 0.85 (95%CI: 0.80 to 0.90) and 0.78 (95%CI: 0.73 to 0.83), but an overestimation in calibration with O:E ratios of 0.65 (95%CI: 0.49 to 0.85) and 0.67 (95%CI: 0.52 to 0.84).

Conclusion

The performance of the models was acceptable in both derivation and validation cohorts. Number of teeth was an additive important predictor of critical outcomes of COVID-19. It is an easy-to-apply tool in hospitals for risk stratification of COVID-19 prognosis.

The predictive value of inflammatory biomarkers for major pathological response in non-small cell lung cancer patients receiving neoadjuvant chemoimmunotherapy and its association with the immune-related tumor microenvironment: a multi-center study

BACKGROUND

Inflammatory biomarkers in the peripheral blood have been established as predictors for immunotherapeutic efficacy in advanced non-small cell lung cancer (NSCLC). Whether they can also predict major pathological response (MPR) in neoadjuvant setting remains unclear.

METHODS

In this multi-center retrospective study, 122 and 92 stage I-IIIB NSCLC patients from six hospitals who received neoadjuvant chemoimmunotherapy followed by surgery were included in the discovery and external validation cohort, respectively. Baseline and on-treatment neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR) and systemic immune-inflammation index (SII) were calculated and associated with MPR. Furthermore, resected tumor samples from 37 patients were collected for RNA-sequencing to investigate the immune-related tumor microenvironment.

RESULTS

In both the discovery and validation cohorts, the on-treatment NLR, dNLR, PLR, and SII levels were significantly lower in the patients with MPR versus non-MPR. On-treatment SII remained an independent predictor of MPR in multivariate logistic regression analysis. The area under the curve (AUC) of on-treatment SII for predicting MPR was 0.75 (95%CI, 0.67-0.84) in the discovery cohort. Moreover, the predictive value was further improved by combining the on-treatment SII and radiological tumor regression data, demonstrating an AUC of 0.82 (95%CI, 0.74-0.90). The predictive accuracy was validated in the external cohort. Compared with the SII-high group, patients with SII-Low were associated with the activated B cell receptor signaling pathway and a higher intratumoral immune cell infiltration level.

CONCLUSIONS

On-treatment SII was independently associated with MPR in NSCLC patients receiving neoadjuvant chemoimmunotherapy. Further prospective studies are warranted.

Adolescent girls in aquaculture ecozones at risk of nutrient deficiency in Bangladesh development and validation of an integrated metric

BACKGROUND

This study developed and validated an integrated metric that enhances understanding of linkages between agro-ecological and socio-economic factors that are important for explaining nutritional wellbeing in relation to fish consumption; especially among adolescent girls who are at risk of nutritional deficiency in Bangladesh. Currently, there is no metric that takes account of environmental, cultural and economic contexts when considering fish consumption and dietary health from a policy perspective.

METHODS

The study was designed as a bi-seasonal survey, repeated in the same population of adolescent girls recruited during the dry and wet seasons. Sampling was stratified by five settings (four aqua-agroecological zones and one processing plant community), with 60 girls recruited in each. Associations between candidate predictors (salinity, diet diversity, religion, socio-economic status and women's autonomy score) and dependent variables representing nutritional outcomes (anthropometry, omega-3 index and micronutrient levels) were explored in multivariable regressions. The fitted model with its predictors was validated, and a risk score derived from responses to a few short questions on religion, salinity zone, female autonomy, diet diversity and tilapia consumption.

RESULTS

The omega-3 index showed the clearest distinction between seasons, by salinity and religion. Higher female autonomy, religion (being Hindu rather than Muslim), geographical location (living in a high or mid-saline area), and a higher dietary diversity were the strongest predictors of whole blood omega-3 index. The c-index for the prognostic model was 0.83 and 0.76 in the wet and dry season respectively, indicating good predictive accuracy. There appeared to be a clear trend in risk scores differentiating between those 'chronically at risk' and those 'never at risk'.

CONCLUSIONS

Observational data on different aquaculture-ecozones defined by salinity enabled us to establish linkages between seasonal fish intake, religion, diet diversity, female autonomy and nutritional wellbeing. The purpose of the metric is to reveal these specific linkages in practice. This tool should improve targeting of timely, preventative and cost-effective nutritional interventions to adolescent girls most at-risk from low omega-3 levels in communities where seafood is produced.

Hospital pharmacists' opinions on a risk prediction tool for medication-related harm in older people

AIM

Older adults are particularly affected by medication-related harm (MRH) during transitions of care. There are no clinical tools predicting those at highest risk of MRH post hospital discharge. The PRIME study (prospective study to develop a model to stratify the risk of MRH in hospitalized patients) developed and internally validated a risk-prediction tool (RPT) that provides a percentage score of MRH in adults over 65 in the 8 weeks following hospital discharge. This qualitative study aimed to explore the views of hospital pharmacists around enablers and barriers to clinical implementation of the PRIME-RPT.

METHODS

Ten hospital pharmacists: (band 6, n = 3; band 7, n = 2; band 8, n = 5) participated in semistructured interviews at the Royal Sussex County Hospital (Brighton, UK). The pharmacists were presented with five case-vignettes each with a calculated PRIME-RPT score to help guide discussion. Case-vignettes were designed to be representative of common clinical encounters. Data were thematically analysed using a "framework" approach.

RESULTS

Seven themes emerged in relation to the PRIME-RPT: (1) providing a medicine-prioritisation aide; (2) acting as a deprescribing alert; (3) facilitating a holistic review of patient medication management; (4) simplifying communication of MRH to patients and the multidisciplinary team; (5) streamlining community follow-up and integration of risk discussion into clinical practice; (6) identifying barriers for the RPTs integration in clinical practice; and (7) acknowledging its limitations.

CONCLUSION

Hospital pharmacists found the PRIME-RPT beneficial in identifying older patients at high risk of MRH following hospital discharge, facilitating prioritising interventions to those at highest risk while still acknowledging its limitations.

Predicting short-term outcomes after transcatheter aortic valve replacement for aortic stenosis

BACKGROUND

The approved use of transcatheter aortic valve replacement (TAVR) for aortic stenosis has expanded substantially over time. However, gaps remain with respect to accurately delineating risk for poor clinical and patient-centered outcomes. Our objective was to develop prediction models for 30-day clinical and patient-centered outcomes after TAVR within a large, diverse community-based population.

METHODS

We identified all adults who underwent TAVR between 2013-2019 at Kaiser Permanente Northern California, an integrated healthcare delivery system, and were monitored for the following 30-day outcomes: all-cause death, improvement in quality of life, all-cause hospitalizations, all-cause emergency department (ED) visits, heart failure (HF)-related hospitalizations, and HF-related ED visits. We developed prediction models using gradient boosting machines using linked demographic, clinical and other data from the Society for Thoracic Surgeons (STS)/American College of Cardiology (ACC) TVT Registry and electronic health records. We evaluated model performance using area under the curve (AUC) for model discrimination and associated calibration plots. We also evaluated the association of individual predictors with outcomes using logistic regression for quality of life and Cox proportional hazards regression for all other outcomes.

RESULTS

We identified 1,565 eligible patients who received TAVR. The risks of adverse 30-day post-TAVR outcomes ranged from 1.3% (HF hospitalizations) to 15.3% (all-cause ED visits). In models with the highest discrimination, discrimination was only moderate for death (AUC 0.60) and quality of life (AUC 0.62), but better for HF-related ED visits (AUC 0.76). Calibration also varied for different outcomes. Importantly, STS risk score only independently predicted death and all-cause hospitalization but no other outcomes. Older age also only independently predicted HF-related ED visits, and race/ethnicity was not significantly associated with any outcomes.

CONCLUSIONS

Despite using a combination of detailed STS/ACC TVT Registry and electronic health record data, predicting short-term clinical and patient-centered outcomes after TAVR remains challenging. More work is needed to identify more accurate predictors for post-TAVR outcomes to support personalized clinical decision making and monitoring strategies.

Prevalence of atrial fibrillation in a regional Victoria setting, findings from the crossroads studies (2001-2003 and 2016-2018)

OBJECTIVE

To estimate the prevalence of atrial fibrillation (AF) in regional Victoria at two time points (2001-2003 and 2016-2018), and to assess the use of electrocardiogram rhythm strips in a rural, community-based study for AF investigation.

DESIGN

Repeated cross-sectional design involving survey of residents of randomly selected households and a clinic. Predictors of AF were assessed using Firth penalised logistic regression, as appropriate for rare events.

SETTING

Goulburn Valley, Victoria.

PARTICIPANTS

Household residents aged ≥16 years. Non-pregnant participants aged 18+ were eligible for the clinic.

MAIN OUTCOME MEASURES

Atrial fibrillation by 12 lead electrocardiogram (earlier study) or electrocardiogram rhythm strip (AliveCor® device) (recent study).

RESULTS

The age standardised prevalence of AF was similar between the two studies (1.6% in the 2001-2003 study and 1.8% in the 2016-2018 study, 95% confidence interval of difference -0.010, 0.014, p = 0.375). The prevalence in participants aged ≥65 years was 3.4% (1.0% new cases) in the recent study. Predictors of AF in the earlier study were male sex, older age and previous stroke, while in the recent study they were previous stroke and self-reported diabetes. AliveCor® traces were successfully classified by the in-built algorithm (91%) vs physician (100%).

CONCLUSION

The prevalence of AF among community-based participants in regional Victoria was similar to predominantly metropolitan-based studies, and was unchanged over time despite increased rates of risk factors. Electrocardiogram rhythm strip investigation was successfully utilised, and particularly benefited from physician overview.

Predicting Postoperative Anemia and Blood Transfusion Following Total Knee Arthroplasty

BACKGROUND

While transfusion and clinically relevant anemia after elective primary total knee arthroplasty (TKA) are uncommon, there remains a question of who needs postoperative hemoglobin monitoring, especially in the setting of increasing incidence of outpatient TKA. The purpose of this study was to create predictive models for postoperative anemia and blood transfusion to guide clinical decision-making.

METHODS

The records of consecutive TKA patients were reviewed from February 2016 to December 2020 at a single institution. Two multivariable logistic regression models, for postoperative anemia (hemoglobin < 10 g/dL) and allogeneic blood transfusion included 8 variables: age, sex, body mass index, preoperative hemoglobin level, tranexamic acid total dose, American Society of Anesthesiologists level, operative time, and drain use. Model performance was assessed using accuracy, area under the curve (AUC), sensitivity, and specificity.

RESULTS

The records of 14,901 patients were included in this study. Patients had a mean (± standard deviation) age of 67.9 ± 9.2 years and mean body mass index of 31.3 ± 6.5 kg/m². The postoperative anemia model had an accuracy of 88% (95% confidence interval [CI], 87%-89%) and AUC of 0.88 (95% CI, 0.87-0.89). The blood transfusion model had an accuracy of 97% (95% CI, 96%-97%) and AUC of 0.90 (95% CI, 0.87-0.93).

CONCLUSION

The postoperative anemia and blood transfusion model accurately predicted each outcome. Patients with less than a 5% probability of postoperative anemia may not benefit from a complete blood count at postoperative day 1. Application of these criteria may save the healthcare system hundreds of millions of dollars.

LEVEL OF EVIDENCE

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

Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network

Worldwide, oral cancer is the sixth most common type of cancer. India is in 2nd position, with the highest number of oral cancer patients. To the population of oral cancer patients, India contributes to almost one-third of the total count. Among several types of oral cancer, the most common and dominant one is oral squamous cell carcinoma (OSCC). The major reason for oral cancer is tobacco consumption, excessive alcohol consumption, unhygienic mouth condition, betel quid eating, viral infection (namely human papillomavirus), etc. The early detection of oral cancer type OSCC, in its preliminary stage, gives more chances for better treatment and proper therapy. In this paper, author proposes a convolutional neural network model, for the automatic and early detection of OSCC, and for experimental purposes, histopathological oral cancer images are considered. The proposed model is compared and analyzed with state-of-the-art deep learning models like VGG16, VGG19, Alexnet, ResNet50, ResNet101, Mobile Net and Inception Net. The proposed model achieved a cross-validation accuracy of 97.82%, which indicates the suitability of the proposed approach for the automatic classification of oral cancer data.

Comparing risk prediction models aimed at predicting hospitalizations for adverse drug events in community dwelling older adults: a protocol paper

Background

The objective of this paper is to describe the creation, validation, and comparison of two risk prediction modeling approaches for community-dwelling older adults to identify individuals at highest risk for adverse drug event-related hospitalizations. One approach will use traditional statistical methods, the second will use a machine learning approach.

Methods

We will construct medication, clinical, health care utilization, and other variables known to be associated with adverse drug event-related hospitalizations. To create the cohort, we will include older adults (≥ 65 years of age) empaneled to a primary care physician within the Cedars-Sinai Health System primary care clinics with polypharmacy (≥ 5 medications) or at least 1 medication commonly implicated in ADEs (certain oral hypoglycemics, anti-coagulants, anti-platelets, and insulins). We will use a Fine-Gray Cox proportional hazards model for one risk modeling approach and DataRobot, a data science and analytics platform, to run and compare several widely used supervised machine learning algorithms, including Random Forest, Support Vector Machine, Extreme Gradient Boosting (XGBoost), Decision Tree, Naïve Bayes, and K-Nearest Neighbors. We will use a variety of metrics to compare model performance and to assess the risk of algorithmic bias.

Discussion

In conclusion, we hope to develop a pragmatic model that can be implemented in the primary care setting to risk stratify older adults to further optimize medication management.

Development and Internal Validation of a Prediction Model for Surgical Success of Maxillomandibular Advancement for the Treatment of Moderate to Severe Obstructive Sleep Apnea

Background: Maxillomandibular advancement (MMA) has been shown to be the most effective surgical therapy for obstructive sleep apnea (OSA). Despite high success rates, there are patients who are considered as non-responders to MMA. In order to triage and inform these patients on their expected prognosis of MMA before the surgery, this study aimed to develop, internally validate, and calibrate a prediction model for the presence of surgical success for MMA in patients with OSA. Methods: A retrospective cohort study was conducted that included patients that had undergone MMA for moderate to severe OSA. Baseline clinical, polysomnographic, cephalometric, and drug-induced sleep endoscopy findings were recorded as potential predictors. Presence or absence of surgical success was recorded as outcome. Binary logistic regression analyses were conducted to develop the model. Performance and clinical values of the model were analyzed. Results: One hundred patients were included, of which sixty-seven (67%) patients reached surgical success. Anterior lower face height (ALFH) (OR: 0.93 [0.87−1.00], p = 0.05), superior posterior airway space (SPAS) (OR: 0.76 [0.62−0.92], p < 0.05), age (OR: 0.96 [0.91−1.01], p = 0.13), and a central apnea index (CAI) <5 events/hour sleep (OR: 0.16 [0.03−0.91], p < 0.05) were significant independent predictors in the model (significance level set at p = 0.20). The model showed acceptable discrimination with a shrunken area under the curve of 0.74, and acceptable calibration. The added predictive values for ruling in and out of surgical success were 0.21 and 0.32, respectively. Conclusions: Lower age at surgery, CAI < 5 events/hour, lower ALFH, and smaller SPAS were significant predictors for the surgical success of MMA. The discrimination, calibration, and clinical added values of the model were acceptable.

Development and Validation of Esophageal Squamous Cell Carcinoma Risk Prediction Models Based on an Endoscopic Screening Program

IMPORTANCE

Assessment tools are lacking for screening of esophageal squamous cell cancer (ESCC) in China, especially for the follow-up stage. Risk prediction to optimize the screening procedure is urgently needed.

OBJECTIVE

To develop and validate ESCC prediction models for identifying people at high risk for follow-up decision-making.

DESIGN, SETTING, AND PARTICIPANTS

This open, prospective multicenter diagnostic study has been performed since September 1, 2006, in Shandong Province, China. This study used baseline and follow-up data until December 31, 2021. The data were analyzed between April 6 and May 31, 2022. Eligibility criteria consisted of rural residents aged 40 to 69 years who had no contraindications for endoscopy. Among 161 212 eligible participants, those diagnosed with cancer or who had cancer at baseline, did not complete the questionnaire, were younger than 40 years or older than 69 years, or were detected with severe dysplasia or worse lesions were eliminated from the analysis.

EXPOSURES

Risk factors obtained by questionnaire and endoscopy.

MAIN OUTCOMES AND MEASURES

Pathological diagnosis of ESCC and confirmation by cancer registry data.

RESULTS

In this diagnostic study of 104 129 participants (56.39% women; mean [SD] age, 54.31 [7.64] years), 59 481 (mean [SD] age, 53.83 [7.64] years; 58.55% women) formed the derivation set while 44 648 (mean [SD] age, 54.95 [7.60] years; 53.51% women) formed the validation set. A total of 252 new cases of ESCC were diagnosed during 424 903.50 person-years of follow-up in the derivation cohort and 61 new cases from 177 094.10 person-years follow-up in the validation cohort. Model A included the covariates age, sex, and number of lesions; model B included age, sex, smoking status, alcohol use status, body mass index, annual household income, history of gastrointestinal tract diseases, consumption of pickled food, number of lesions, distinct lesions, and mild or moderate dysplasia. The Harrell C statistic of model A was 0.80 (95% CI, 0.77-0.83) in the derivation set and 0.90 (95% CI, 0.87-0.93) in the validation set; the Harrell C statistic of model B was 0.83 (95% CI, 0.81-0.86) and 0.91 (95% CI, 0.88-0.95), respectively. The models also had good calibration performance and clinical usefulness.

CONCLUSIONS AND RELEVANCE

The findings of this diagnostic study suggest that the models developed are suitable for selecting high-risk populations for follow-up decision-making and optimizing the cancer screening process.

Multivariable regression: understanding one of medicine's most fundamental statistical tools

Multivariable regression is a fundamental tool that drives observational research in orthopaedic surgery. However, regression analyses are not always implemented correctly. This study presents a basic overview of regression analyses and reviews frequent points of confusion. Topics include linear, logistic, and time-to-event regressions, causal inference, confounders, overfitting, missing data, multicollinearity, interactions, and key differences between multivariable versus multivariate regression. The goal is to provide clarity regarding the use and interpretation of multivariable analyses for those attempting to increase their statistical literacy in orthopaedic research.

An International Continence Society (ICS)/ International Urogynecological Association (IUGA) joint report on the terminology for the assessment and management of obstetric pelvic floor disorders

AIMS

The terminology of obstetric pelvic floor disorders should be defined and reported as part of a wider clinically oriented consensus.

METHODS

This Report combines the input of members of two International Organizations, the International Continence Society (ICS) and the International Urogynecological Association (IUGA). The process was supported by external referees. Appropriate clinical categories and a sub-classification were developed to give coding to definitions. An extensive process of 12 main rounds of internal and 2 rounds of external review was involved to exhaustively examine each definition, with decision-making by consensus.

RESULTS

A terminology report for obstetric pelvic floor disorders, encompassing 357 separate definitions, has been developed. It is clinically-based with the most common diagnoses defined. Clarity and user-friendliness have been key aims to make it usable by different specialty groups and disciplines involved in the study and management of pregnancy, childbirth and female pelvic floor disorders. Clinical assessment, investigations, diagnosis, conservative and surgical treatments are major components. Illustrations have been included to supplement and clarify the text. Emerging concepts, in use in the literature and offering further research potential but requiring further validation, have been included as an Appendix. As with similar reports, interval (5-10 year) review is anticipated to maintain relevance of the document and ensure it remains as widely applicable as possible.

CONCLUSION

A consensus-based Terminology Report for obstetric pelvic floor disorders has been produced to support clinical practice and research.

Strategies reducing risk of surgical-site infection following pediatric spinal deformity surgery

BACKGROUND

Identifying beneficial preventive strategies for surgical-site infection (SSI) in individual patients with different clinical and surgical characteristics is challenging. The purpose of this study was to investigate the association between preventive strategies and patient risk of SSI taking into consideration baseline risks and estimating the reduction of SSI probability in individual patients attributed to these strategies.

METHODS

Pediatric patients who underwent primary, revision, or final fusion for their spinal deformity at 7 institutions between 2004 and 2018 were included. Preventive strategies included the use of topical vancomycin, bone graft, povidone-iodine (PI) irrigations, multilayered closure, impermeable dressing, enrollment in quality improvement (QI) programs, and adherence to antibiotic prophylaxis. The CDC definition of SSI as occurring within 90 days postoperatively was used. Multiple regression modeling was performed following multiple imputation and multicollinearity testing to investigate the effect of preventive strategies on SSI in individual patients adjusted for patient and surgical characteristics.

RESULTS

Univariable regressions demonstrated that enrollment in QI programs and PI irrigation were significantly associated, and topical vancomycin, multilayered closure, and correct intraoperative dosing of antibiotics trended toward association with reduction of SSI. In the final prediction model using multiple regression, enrollment in QI programs remained significant and PI irrigation had an effect in decreasing risks of SSI by average of 49% and 18%, respectively, at the individual patient level.

CONCLUSION

Considering baseline patient characteristics and predetermined surgical and hospital factors, enrollment in QI programs and PI irrigation reduce the risk of SSI in individual patients. Multidisciplinary efforts should be made to implement these practices to increase patient safety.

LEVEL OF EVIDENCE

Prognostic level III study.

Validity of three risk prediction models for dementia or cognitive impairment in Australia

BACKGROUND

no studies have compared the predictive validity of different dementia risk prediction models in Australia.

OBJECTIVES

(i) to investigate the predictive validity of the Australian National University-Alzheimer's Disease Risk Index (ANU-ADRI), LIfestyle for BRAin Health (LIBRA) Index and cardiovascular risk factors, ageing and dementia study (CAIDE) models for predicting probable dementia/cognitive impairment in an Australian cohort. (ii) To develop and assess the predictive validity of a new hybrid model combining variables from the three models.

METHODS

the Hunter Community Study (HCS) included 3,306 adults aged 55-85 years with a median follow-up of 7.1 years. Probable dementia/cognitive impairment was defined using Admitted Patient Data Collection, dispensing of cholinesterase inhibitors or memantine, or a cognitive test. Model validity was assessed by calibration and discrimination. A hybrid model was developed using deep neural network analysis, a machine learning method.

RESULTS

120 (3.6%) participants developed probable dementia/cognitive impairment. Mean calibration by ANU-ADRI, LIBRA, CAIDE and the hybrid model was 19, 0.5, 4.7 and 3.4%, respectively. The discrimination of the models was 0.65 (95% CI 0.60-0.70), 0.65 (95% CI 0.60-0.71), 0.54 (95% CI 0.49-0.58) and 0.80 (95% CI 0.78-0.83), respectively.

CONCLUSION

ANU-ADRI and LIBRA were better dementia prediction tools than CAIDE for identification of high-risk individuals in this cohort. ANU-ADRI overestimated and LIBRA underestimated the risk. The new hybrid model had a higher predictive performance than the other models but it needs to be validated independently in longitudinal studies.

Clinical characteristics do not reliably identify non-adherence in patients with uncontrolled hypertension

PURPOSE

Chemical adherence testing is a reliable method to assess adherence to antihypertensive drugs. However, it is expensive and has limited availability in clinical practice. To reduce the number and costs of chemical adherence tests, we aimed to develop and validate a clinical screening tool to identify patients with a low probability of non-adherence in patients with uncontrolled hypertension.

MATERIALS AND METHODS

In 495 patients with uncontrolled hypertension referred to the University Medical Centre Utrecht (UMCU), the Netherlands, a penalised logistic regression model including seven pre-specified easy-to-measure clinical variables was derived to estimate the probability of non-adherence. Non-adherence was defined as not detecting at least one of the prescribed antihypertensive drugs in plasma or urine. Model performance and test characteristics were evaluated in 240 patients with uncontrolled hypertension referred to the Heartlands Hospital, United Kingdom.

RESULTS

Prevalence of non-adherence to antihypertensive drugs was 19% in the UMCU and 44% in the Heartlands Hospital population. After recalibration of the model's intercept, predicted probabilities agreed well with observed frequencies. The c-statistic of the model was 0.63 (95%CI 0.53-0.72). Predicted probability cut-off values of 15%-22.5% prevented testing in 5%-15% of the patients, carrying sensitivities between 97% (64-100) and 90% (80-95), and negative predictive values between 74% (10-99) and 70% (50-85).

CONCLUSION

The combination of seven clinical variables is not sufficient to reliably discriminate adherent from non-adherent individuals to safely reduce the number of chemical adherence tests. This emphasises the complex nature of non-adherence behaviour and thus the need for objective chemical adherence tests in patients with uncontrolled hypertension.

Risk evaluation of cognitive impairment in patients with heart failure: A call for action

Background

Cognitive impairment (CI) is common in patients with heart failure (HF) and impacts treatment adherence and other aspects of patient life in HF. Recognition of CI in patients with HF is therefore important. We aimed to develop a risk model with easily available patient characteristics, to identify patients with HF who are at high risk to be cognitively impaired and in need for further cognitive investigation.

Methods & results

The risk model was developed in 611 patients ≥ 60 years with HF from the TIME-CHF trial. Fifty-six (9 %) patients had CI (defined as Hodkinson Abbreviated Mental Test ≤ 7). We assessed the association between potential predictors and CI with least-absolute-shrinkage-and-selection-operator (LASSO) regression analysis. The selected predictors were: older age, female sex, NYHA class III or IV, Charlson comorbidity index ≥ 6, anemia, heart rate ≥ 70 bpm and systolic blood pressure ≥ 145 mmHg. A model that combined these variables had a c-statistic of 0.70 (0.63-0.78). The model was validated in 155 patients ≥ 60 years with HF from the ECHO study. In the validation cohort 51 (33 %) patients had CI (defined as a Mini Mental State Exam ≤ 24). External validation showed an AUC of 0.56 (0.46-0.66).

Conclusions

This risk model with easily available patient characteristics has poor predictive performance in external validation, which may be due to case-mix variation. These findings underscore the need for active screening and standardized assessment for CI in patients with HF.

Machine learning models predicting undertriage in telephone triage

BACKGROUND

Undertriaged patients have worse outcomes than appropriately triaged patients. Machine learning provides better triage prediction than conventional triage in emergency departments, but no machine learning-based undertriage prediction models have yet been developed for prehospital telephone triage. We developed and validated machine learning models for telephone triage.

MATERIALS AND METHODS

We conducted a retrospective cohort study with the largest after-hour house-call (AHHC) service dataset in Japan. Participants were ≥16 years and used the AHHC service between 1 November 2018 and 31 January 2021. We developed five prediction models based on support vector machine (SVM), lasso regression (LR), random forest (RF), gradient-boosted decision tree (XGB), and deep neural network (DNN). The primary outcome was undertriage, and predictors were telephone triage level and routinely available telephone-based data, including age, sex, 80 chief complaint categories and 10 comorbidities. We measured the area under the receiver operating characteristic curve (AUROC) for all the models.

RESULTS

We identified 15,442 eligible patients (age: 38.4 ± 16.6, male: 57.2%), including 298 (1.9%; age: 58.2 ± 23.9, male: 55.0%) undertriaged patients. RF and XGB outperformed the other models, with the AUROC values (95% confidence interval; 95% CI) of the SVM, LR, RF, XGB and DNN for undertriage being 0.62 (0.55-0.69), 0.79 (0.74-0.83), 0.81 (0.76-0.86), 0.80 (0.75-0.84) and 0.77 (0.73-0.82), respectively.

CONCLUSIONS

We found that RF and XGB outperformed other models. Our findings suggest that machine learning models can facilitate the early detection of undertriage and early intervention to yield substantially improved patient outcomes.KEY MESSAGESUndertriaged patients experience worse outcomes than appropriately triaged patients; thus, we developed machine learning models for predicting undertriage in the prehospital setting. In addition, we identified the predictors of risk factors associated with undertriage.Random forest and gradient-boosted decision tree models demonstrated better prediction performance, and the models identified the risk factors associated with undertriage.Machine learning models aid in the early detection of undertriage, leading to significantly improved patient outcomes and identifying undertriage-associated risk factors, including chief complaint categories, could help prioritize conventional telephone triage protocol revision.

2022 American College of Rheumatology/EULAR Classification Criteria for Giant Cell Arteritis

OBJECTIVE

To develop and validate updated classification criteria for giant cell arteritis (GCA).

METHODS

Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in 6 phases: 1) identification of candidate items, 2) prospective collection of candidate items present at the time of diagnosis, 3) expert panel review of cases, 4) data-driven reduction of candidate items, 5) derivation of a points-based risk classification score in a development data set, and 6) validation in an independent data set.

RESULTS

The development data set consisted of 518 cases of GCA and 536 comparators. The validation data set consisted of 238 cases of GCA and 213 comparators. Age ≥50 years at diagnosis was an absolute requirement for classification. The final criteria items and weights were as follows: positive temporal artery biopsy or temporal artery halo sign on ultrasound (+5); erythrocyte sedimentation rate ≥50 mm/hour or C-reactive protein ≥10 mg/liter (+3); sudden visual loss (+3); morning stiffness in shoulders or neck, jaw or tongue claudication, new temporal headache, scalp tenderness, temporal artery abnormality on vascular examination, bilateral axillary involvement on imaging, and fluorodeoxyglucose-positron emission tomography activity throughout the aorta (+2 each). A patient could be classified as having GCA with a cumulative score of ≥6 points. When these criteria were tested in the validation data set, the model area under the curve was 0.91 (95% confidence interval [95% CI] 0.88-0.94) with a sensitivity of 87.0% (95% CI 82.0-91.0%) and specificity of 94.8% (95% CI 91.0-97.4%).

CONCLUSION

The 2022 American College of Rheumatology/EULAR GCA classification criteria are now validated for use in clinical research.

Predictors of pain reduction following a program of manual therapies for patients with temporomandibular disorders: A prospective observational study

BACKGROUND

Clinical guidelines recommend conservative treatment for the management of temporomandibular disorders (TMD), and manual therapy (MT) is commonly applied to reduce pain and improve function.

OBJECTIVES

To identify predictors of pain reduction and functional improvement following a program of manual therapies (MTP) in patients with TMD and develop a first screening tool that could be used in clinical practice to facilitate decision-making.

DESIGN

A cohort of 102 adults with a diagnosis of TMD were treated with four weekly sessions within a MTP applied to craniomandibular structures. Candidate predictors were demographic variables, general health variables, psychosocial features, TMD characteristics and related clinical tests. A reduction of pain intensity by at least 30% after the MTP was considered a good outcome. Logistic regression was adopted to develop the predictive model and its performance was assessed considering the explained variance, calibration, and discrimination. Internal validation of the prediction models was further evaluated in 500 bootstrapped samples.

RESULTS

Patients experiencing pain intensity greater than 2/10 during mouth opening, positive expectations of outcome following a MTP, pain localized in the craniocervical region, and a low Central Sensitization Inventory score obtained a good outcome following the MTP. Predictive performance of the identified physical and psychological variables was characterized by high explained variance (R2 = 58%) and discrimination (AUC = 89%) after internal validation. A preliminary screening clinical tool was developed and presented as a nomogram.

CONCLUSIONS

The high discrimination of the prediction model revealed promising findings, although these need to be externally validated in future research.

TRIAL REGISTRATION NUMBER

NCT03990662.

Amphotericin B plus fluorocytosine combined with voriconazole for the treatment of non-HIV and non-transplant-associated cryptococcal meningitis: a retrospective study

Background

Our previous study explored Amphotericin B (AMB) plus 5-flucytosine (5-FC) combined with fluconazole (FLU) therapy in the induction period, which seemed to be better than the previous AMB + 5-FC antifungal therapy in non-HIV and non-transplant-associated CM. However, based on our clinical finding, the outcomes of some CM patients who received AMB plus 5-FC combined with FLU antifungal therapy were still poor. Therefore, we need to explore new antifungal methods in non-HIV and non-transplant-associated CM during the induction period.

Methods

Clinical data from 148 patients admitted to the Third Affiliated Hospital of Sun Yat Sen University from January 2011 to December 2020 were collected. These patients were stratified based on antifungal treatment methods in the induction period (group I with AMB + 5-FC + VOR, group II with AMB + 5-FC + FLU, group III with AMB + 5-FC).

Results

The first hospitalization time of Group I (median: 25 days, IQR: 20–34.5) was significantly shorter than that of Group II (median: 43 days, IQR: 29–62) (p < 0.001) and Group III (median: 50.5 days, IQR: 43–77.5) (p < 0.001). After 2 weeks of follow-up, Group I (26/49) had more patients reaching CSF clearance (p = 0.004) than Group II (18/71) and Group III (7/28). In multivariable analysis, Group II (OR: 3.35, 95%CI 1.43–7.82, p = 0.005) and Group III (OR: 3.8, 95%CI 1.23–11.81, p = 0.021) were associated with higher risk about CSF clearance failure at 2 weeks follow-up than Group I. After 10 weeks of follow-up, the incidence of hypokalemia in Group I was significantly lower than that in Group II (p = 0.003) and Group III (p = 0.004), and the incidence of gastrointestinal discomfort in Group I was significantly lower than that in Group II (p = 0.004).

Conclusion

AMB plus 5-FC combined with VOR may rapidly improve clinical manifestation, decrease CSF OP and clear the cryptococci in CSF during the early phase, substantially shorten the hospitalization time, and reduce the incidences of hypokalemia and gastrointestinal discomfort.

2022 American College of Rheumatology/EULAR classification criteria for giant cell arteritis

OBJECTIVE

To develop and validate updated classification criteria for giant cell arteritis (GCA).

METHODS

Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in six phases: (1) identification of candidate items, (2) prospective collection of candidate items present at the time of diagnosis, (3) expert panel review of cases, (4) data-driven reduction of candidate items, (5) derivation of a points-based risk classification score in a development data set and (6) validation in an independent data set.

RESULTS

The development data set consisted of 518 cases of GCA and 536 comparators. The validation data set consisted of 238 cases of GCA and 213 comparators. Age ≥50 years at diagnosis was an absolute requirement for classification. The final criteria items and weights were as follows: positive temporal artery biopsy or temporal artery halo sign on ultrasound (+5); erythrocyte sedimentation rate ≥50 mm/hour or C reactive protein ≥10 mg/L (+3); sudden visual loss (+3); morning stiffness in shoulders or neck, jaw or tongue claudication, new temporal headache, scalp tenderness, temporal artery abnormality on vascular examination, bilateral axillary involvement on imaging and fluorodeoxyglucose-positron emission tomography activity throughout the aorta (+2 each). A patient could be classified as having GCA with a cumulative score of ≥6 points. When these criteria were tested in the validation data set, the model area under the curve was 0.91 (95% CI 0.88 to 0.94) with a sensitivity of 87.0% (95% CI 82.0% to 91.0%) and specificity of 94.8% (95% CI 91.0% to 97.4%).

CONCLUSION

The 2022 American College of Rheumatology/EULAR GCA classification criteria are now validated for use in clinical research.

Overbite recognition and factors affecting esthetic tolerance among laypeople

OBJECTIVES

To determine recognition ability and the levels of esthetic tolerance of deep bite and anterior open bite (AOB) among laypeople and investigate the factors affecting levels of tolerance.

MATERIALS AND METHODS

Using a questionnaire, laypeople (N = 100) were examined, and overbite was measured. They were tested for whether they recognized deep bite and AOB. Esthetic tolerance thresholds for deep bite and AOB were selected by incremental depiction in grayscale images. Stepwise logistic regression analyses were used to quantify the effect of recognition and other factors (age, sex, education level, occupation, history of orthodontic treatment, interest in orthodontic treatment or retreatment, and overbite presence) affecting the tolerance of overbite problems (α = 0.05).

RESULTS

Of the participants, 55% and 94% recognized deep bite and AOB, respectively. Participants with a deep bite were significantly more likely to esthetically tolerate deep bite compared with those without a deep bite (odds ratio [OR], 3.57; 95% confidence interval [CI], 1.29-9.89). Participants who recognized a deep bite problem had significantly lower esthetic tolerance to deep bite compared with participants who did not recognize a deep bite (OR, 0.17; 95% CI, 0.06-0.45). None of the other eight chosen factors significantly affected the tolerance level of AOB (P > .05).

CONCLUSIONS

Participants with a deep bite or those who did not recognize a deep bite had significantly higher esthetic tolerance of deep bite than those without or those who recognized the problem (P < .05).

Demographic, behavioural and occupational risk factors associated with SARS-CoV-2 infection in UK healthcare workers: a retrospective observational study

OBJECTIVE

Healthcare workers (HCWs) are at higher risk of SARS-CoV-2 infection than the general population. This group is pivotal to healthcare system resilience during the COVID-19, and future, pandemics. We investigated demographic, social, behavioural and occupational risk factors for SARS-CoV-2 infection among HCWs.

DESIGN/SETTING/PARTICIPANTS

HCWs enrolled in a large-scale sero-epidemiological study at a UK university teaching hospital were sent questionnaires spanning a 5-month period from March to July 2020. In a retrospective observational cohort study, univariate logistic regression was used to assess factors associated with SARS-CoV-2 infection. A Least Absolute Shrinkage Selection Operator regression model was used to identify variables to include in a multivariate logistic regression model.

RESULTS

Among 2258 HCWs, highest ORs associated with SARS-CoV-2 antibody seropositivity on multivariate analysis were having a household member previously testing positive for SARS-CoV-2 antibodies (OR 6.94 (95% CI 4.15 to 11.6); p&lt;0.0001) and being of black ethnicity (6.21 (95% CI 2.69 to 14.3); p&lt;0.0001). Occupational factors associated with a higher risk of seropositivity included working as a physiotherapist (OR 2.78 (95% CI 1.21 to 6.36); p=0.015) and working predominantly in acute medicine (OR 2.72 (95% CI 1.57 to 4.69); p&lt;0.0001) or medical subspecialties (not including infectious diseases) (OR 2.33 (95% CI 1.4 to 3.88); p=0.001). Reporting that adequate personal protective equipment (PPE) was 'rarely' available had an OR of 2.83 (95% CI 1.29 to 6.25; p=0.01). Reporting attending a handover where social distancing was not possible had an OR of 1.39 (95% CI 1.02 to 1.9; p=0.038).

CONCLUSIONS

The emergence of SARS-CoV-2 variants and potential vaccine escape continue to threaten stability of healthcare systems worldwide, and sustained vigilance against HCW infection remains a priority. Enhanced risk assessments should be considered for HCWs of black ethnicity, physiotherapists and those working in acute medicine or medical subspecialties. Workplace risk reduction measures include ongoing access to high-quality PPE and effective social distancing measures.

Prognosis of lasso-like penalized Cox models with tumor profiling improves prediction over clinical data alone and benefits from bi-dimensional pre-screening

BACKGROUND

Prediction of patient survival from tumor molecular '-omics' data is a key step toward personalized medicine. Cox models performed on RNA profiling datasets are popular for clinical outcome predictions. But these models are applied in the context of "high dimension", as the number p of covariates (gene expressions) greatly exceeds the number n of patients and e of events. Thus, pre-screening together with penalization methods are widely used for dimensional reduction.

METHODS

In the present paper, (i) we benchmark the performance of the lasso penalization and three variants (i.e., ridge, elastic net, adaptive elastic net) on 16 cancers from TCGA after pre-screening, (ii) we propose a bi-dimensional pre-screening procedure based on both gene variability and p-values from single variable Cox models to predict survival, and (iii) we compare our results with iterative sure independence screening (ISIS).

RESULTS

First, we show that integration of mRNA-seq data with clinical data improves predictions over clinical data alone. Second, our bi-dimensional pre-screening procedure can only improve, in moderation, the C-index and/or the integrated Brier score, while excluding irrelevant genes for prediction. We demonstrate that the different penalization methods reached comparable prediction performances, with slight differences among datasets. Finally, we provide advice in the case of multi-omics data integration.

CONCLUSIONS

Tumor profiles convey more prognostic information than clinical variables such as stage for many cancer subtypes. Lasso and Ridge penalizations perform similarly than Elastic Net penalizations for Cox models in high-dimension. Pre-screening of the top 200 genes in term of single variable Cox model p-values is a practical way to reduce dimension, which may be particularly useful when integrating multi-omics.

A noninvasive scoring model for the differential diagnosis of ACTH-dependent Cushing's syndrome: a retrospective analysis of 311 patients based on easy-to-use parameters

OBJECTIVES

The differential diagnosis of ACTH-dependent Cushing's disease (CS) is challenging. The gold standard approach bilateral inferior petrosal sinus sampling (BIPSS) is expensive and invasive, while other noninvasive tests, like the high-dose dexamethasone suppression test (HDDST), provide unsatisfactory diagnostic accuracy. This study aimed to find a new noninvasive practical approach with higher diagnostic accuracy to differently diagnose ACTH-dependent CS, which can be used in centers where BIPSS cannot be applied.

METHODS

264 Cushing's disease (CD) patients and 47 ectopic ACTH secretion syndrome (EAS) patients were analyzed in this single-center retrospective study (2011-2021). The multivariate logistic model was used to construct the scoring model.

RESULTS

Female (adjusted OR 3.030, 95%CI 1.229-7.471), hypokalemia (0.209, 0.076-0.576), ACTH (0.988, 0.982-0.994), MRI pituitary lesion positive (8.671, 3.521-21.352), and HDDST positive (2.768, 1.139-6.726) have a strong association with the differential diagnosis of ACTH-dependent CS and were included in the final multivariable logistic regression model. A -14-to-14-point noninvasive scoring model was built on the model. The AUC of the noninvasive scoring model was 0.915 (95% CI 0.869-0.960), significantly higher than the AUC of HDDST (0.756, 95% CI 0.685-0.825, P = 0.004). The optimal cutoff of the model was ≥0 to diagnose CD. The sensitivity of the noninvasive scoring model was 91.3% (95% CI 87.3%-94.1%), and the specificity was 80.9% (95% CI 67.5%-89.6%). When the model's sensitivity was 100.0%, the cutoff was ≥ -10 with a specificity of 19.2%; when the model's specificity was 100.0%, the cutoff was ≥ 13 with a sensitivity of 22.7%.

CONCLUSIONS

We developed a noninvasive scoring model to distinguish CD and EAS in ACTH-dependent CS patients with higher diagnostic utility than HDDST in the same cohort. The noninvasive scoring model might be applied in areas where BIPSS is unavailable, the CRH is hard to obtain, or the desmopressin stimulation is not widely applied. It also provided a triage tool for selecting patients that might benefit the most from a further BIPSS test.

Machine learning-guided covariate selection for time-to-event models developed from a small sample of real-world patients receiving bevacizumab treatment

Therapeutic outcomes in patients with metastatic colorectal cancer (mCRC) receiving bevacizumab treatment are highly variable, and a reliable predictive factor is not available. Progression-free survival (PFS) and overall survival (OS) were recorded from an observational, prospective study after 5 years of follow-up, including 46 patients with mCRC receiving bevacizumab treatment. Three vascular endothelial growth factor (VEGF)-A and two intercellular adhesion molecule-1 genes polymorphisms, age, gender, weight, dosing scheme, and co-treatments were collected. Given the relatively small number of events (37 [80%] for the PFS and 26 [57%] for the OS), to study the effect of these covariates on PFS and OS, a covariate analysis was performed using statistical and supervised machine learning techniques, including Cox regression, penalized Cox regression techniques (least absolute shrinkage and selection operator [LASSO], ridge regression, and elastic net), survival trees, and survival forest. The predictive performance of each method was evaluated in bootstrapped samples, using prediction error curves and the area under the curve of the receiver operating characteristic. The LASSO penalized Cox-regression model showed the best overall performance. Nonlinear mixed effects (NLME) models were developed, and a conventional stepwise covariate search was performed. Then, covariates identified as important by the LASSO model were included in the base NLME models developed for PFS and OS, resulting in improved models as compared to those obtained with the stepwise covariate search. It was shown that having gene polymorphisms in VEGFA (rs699947 and rs1570360) and ICAM1 (rs1799969) are associated with a favorable clinical outcome in patients with mCRC receiving bevacizumab treatment.

Successful stopping of biologic therapy for remission in children and young people with juvenile idiopathic arthritis

OBJECTIVES

Clinicians concerned about long-term safety of biologics in JIA may consider tapering or stopping treatment once remission is achieved despite uncertainty in maintaining drug-free remission. This analysis aims to (i) calculate how many patients with JIA stop biologics for remission, (ii) calculate how many later re-start therapy and after how long, and (iii) identify factors associated with re-starting biologics.

METHODS

Patients starting biologics between 1 January 2010 and 7 September 2021 in the UK JIA Biologics Register were included. Patients stopping biologics for physician-reported remission, those re-starting biologics and factors associated with re-starting, were identified. Multiple imputation accounted for missing data.

RESULTS

Of 1451 patients with median follow-up of 2.7 years (IQR 1.4, 4.0), 269 (19%) stopped biologics for remission after a median of 2.2 years (IQR 1.7, 3.0). Of those with follow-up data (N = 220), 118 (54%) later re-started therapy after a median of 4.7 months, with 84% re-starting the same biologic. Patients on any-line tocilizumab (prior to stopping) were less likely to re-start biologics (vs etanercept; odds ratio [OR] 0.3; 95% CI: 0.2, 0.7), while those with a longer disease duration prior to biologics (OR 1.1 per year increase; 95% CI: 1.0, 1.2) or prior uveitis were more likely to re-start biologics (OR 2.5; 95% CI: 1.3, 4.9).

CONCLUSIONS

This analysis identified factors associated with successful cessation of biologics for remission in JIA as absence of uveitis, prior treatment with tocilizumab and starting biologics earlier in the disease course. Further research is needed to guide clinical recommendations.

Prognostic Modelling Studies of Coronary Heart Disease—A Systematic Review of Conventional and Genetic Risk Factor Studies

This study aims to provide an overview of multivariable prognostic modelling studies developed for coronary heart disease (CHD) in the general population and to explore the optimal prognostic model by comparing the models’ performance. A systematic review was performed using Embase, PubMed, Cochrane, Web of Science, and Scopus databases until 30 November 2019. In this work, only prognostic studies describing conventional risk factors alone or a combination of conventional and genomic risk factors, being developmental and/or validation prognostic studies of a multivariable model, were included. A total of 4021 records were screened by titles and abstracts, and 72 articles were eligible. All the relevant studies were checked by comparing the discrimination, reclassification, and calibration measures. Most of the models were developed in the United States and Canada and targeted the general population. The models included a set of similar predictors, such as age, sex, smoking, cholesterol level, blood pressure, BMI, and diabetes mellitus. In this study, many articles were identified and screened for consistency and reliability using CHARM and GRIPS statements. However, the usefulness of most prognostic models was not demonstrated; only a limited number of these models supported clinical evidence. Unfortunately, substantial heterogeneity was recognized in the definition and outcome of CHD events. The inclusion of genetic risk scores in addition to conventional risk factors might help in predicting the incidence of CHDs; however, the generalizability of the existing prognostic models remains open. Validation studies for the existing developmental models are needed to ensure generalizability, improve the research quality, and increase the transparency of the study.

Hypertension among women in reproductive age in India: Can we predict the risk? An analysis from National Family Health Survey (2015-2016)

Background

Hypertension in women of reproductive age group is of special concern because of the vulnerability of women to pregnancy-induced hypertension apart from socio-cultural vulnerability.

Aim

The objective of the study was to identify the predictors for hypertension among Indian women and to develop a risk score which would provide an opportunity for early detection and appropriate action.

Material and Methods

This study was based on the data collected in National Family Health Survey in 2015-2016. Women in India of 15-49 years were the study population. Data were analysed using SPSS v17. Logistic regression analysis was carried and expressed as odds ratio with 95% confidence intervals to identify predictors of hypertension. The risk score for hypertension was developed after shrinkage of variables and by using regression coefficients obtained by standard Logistic Regression Model.

Results

Among 6,87,230 women between 15 and 49 years, 77,788 (11.3%) were hypertensive. The study results revealed that there was an increasing trend in the prevalence of hypertension (26.5%) with increasing age, and with increasing weight (23.4%). Urban areas (12.3% vs 10.9%), alcoholics (19.2%) and various forms of tobacco users (14.8%) had more prevalence of hypertension.

Conclusion

Age, residing in urban area, consuming tobacco products, consumption of alcohol, non-vegetarian diet and overweight, were found to be the significant predictor variables, and were used to develop the Risk Prediction score using logistic regression model.

Association between time to advanced airway management and survival during pediatric out-of-hospital cardiac arrest

Background

Respiratory care, including advanced airway management (AAM), is an important part of pediatric resuscitation. This study aimed to determine whether time to AAM is associated with outcomes after out-of-hospital cardiac arrest (OHCA) in children.

Methods

This was a nationwide population-based observational study using the Japanese government-led registry of OHCA patients. Children (aged 1–17 years) who experienced OHCA and received AAM by emergency medical service (EMS) personnel in the prehospital setting from 2014 to 2019 were included. Multivariable logistic regression models were used to assess the associations between time to AAM (defined as time in minutes from emergency call to the first successful AAM) and outcomes after OHCA. The primary outcome was one-month overall survival. The secondary outcomes were prehospital return of spontaneous circulation (ROSC) and one-month neurologically favorable survival.

Results

A total of 761 patients (mean [SD] age, 12.7 [4.8] years) were included. The mean time to AAM was 18.9 min (SD, 7.9). Overall, 77 (10.1%) patients survived one month after OHCA. After adjusting for potential confounders, longer time to AAM was significantly associated with a decreased chance of one-month survival (multivariable adjusted OR per minute delay, 0.93 [95% CI, 0.89–0.97]; P = 0.001). Similar association was observed for prehospital ROSC (adjusted OR, 0.94 [95% CI, 0.90–0.99]; P = 0.01) and neurologically favorable survival (adjusted OR, 0.83 [95% CI, 0.72–0.95]; P = 0.006). This association between time to AAM and survival was consistent across a variety of sensitivity and subgroup analyses.

Conclusions

Among pediatric OHCA patients, delayed AAM was associated with a decreased chance of survival, although the influence of resuscitation time bias might remain.

Predicting real world spatial disorientation in Alzheimer's disease patients using virtual reality navigation tests

Spatial navigation impairments in Alzheimer's disease (AD) have been suggested to underlie patients experiencing spatial disorientation. Though many studies have highlighted navigation impairments for AD patients in virtual reality (VR) environments, the extent to which these impairments predict a patient's risk for spatial disorientation in the real world is still poorly understood. The aims of this study were to (a) investigate the spatial navigation abilities of AD patients in VR environments as well as in a real world community setting and (b) explore whether we could predict patients at a high risk for spatial disorientation in the community based on their VR navigation. Sixteen community-dwelling AD patients and 21 age/gender matched controls were assessed on their egocentric and allocentric navigation abilities in VR environments using the Virtual Supermarket Test (VST) and Sea Hero Quest (SHQ) as well as in the community using the Detour Navigation Test (DNT). When compared to controls, AD patients exhibited impairments on the VST, SHQ, and DNT. For patients, only SHQ wayfinding distance and wayfinding duration significantly predicted composite disorientation score on the DNT (β = 0.422, p = 0.034, R² = 0.299 and β = 0.357, p = 0.046, R² = 0.27 respectively). However, these same VR measures could not reliably predict which patients were at highest risk of spatial disorientation in the community (p > 0.1). Future studies should focus on developing VR-based tests which can predict AD patients at high risk of getting spatially disorientated in the real world.

Development of a risk calculator to predict attention-deficit/hyperactivity disorder in very preterm/very low birth weight newborns

BACKGROUND

Very preterm/very low birth weight (VP/VLBW) newborns can have lifelong morbidities, as attention-deficit/hyperactivity disorder (ADHD). Clinicians have no markers to discriminate which among those individuals will develop later ADHD, based only on the clinical presentation at birth. Our aim was to develop an individualized risk calculator for ADHD in VP/VLBW newborns.

METHODS

This retrospective prognostic study included a consecutive sample of all VP/VLBW children (gestational age <32 weeks and/or birth weight <1.5 kg) born between 2010 and 2012 from a clinical cohort in a Brazilian tertiary care hospital. Children were clinically assessed at 6 years of age for ADHD using the Schedule for Affective Disorders and Schizophrenia for School-Age Children (K-SADS). The least absolute shrinkage and selection operator (LASSO) method was used for model-building.

RESULTS

Ninety-six VP/VLBW children were assessed at 6 years of age (92% follow-up), of whom 32 (33%) were diagnosed with ADHD. The area under the ROC curve (AUC) for ADHD prediction based on seven parameters (late-onset sepsis confirmed by blood culture, necrotizing enterocolitis, neonatal seizures, periventricular leukomalacia, respiratory distress syndrome, length of hospital stay, and number of maternal ADHD symptoms) was .875 (CI, 0.800-0.942, p < .001; AUC corrected for optimism with bootstrapping: .806), a performance that is comparable to other medical risk calculators. Compared to approaches that would offer early intervention to all, or intervention to none, the risk calculator will be more useful in selecting VP/VLBW newborns, with statistically significant net benefits at cost:benefits of around 1:2 to around 10:6 (range of ADHD risk thresholds of 32%-62%, respectively). It also showed specificity for ADHD compared to other prevalent child psychopathologies.

CONCLUSIONS

The risk calculator showed good performance for early identification of VP/VLBW newborns at high risk of future ADHD diagnosis. External validity in population-based samples is needed to extend clinical usefulness.

Development and Validation of a Nomogram for the Prediction of Unfavorable Treatment Outcome Among Multi-Drug Resistant Tuberculosis Patients in North West Ethiopia: An Application of Prediction Modelling

Background

Multidrug-resistant tuberculosis (MDR-TB) is a global problem and a health security threat, which makes “Ending the global TB epidemic in 2035” unachievable. Globally, the unfavourable treatment outcome remains unacceptably high. Therefore, this study aimed to develop a risk prediction model for unfavorable treatment outcomes in MDR-TB patients, which can be used by clinicians as a simple clinical tool in their decision-making.

Objective

The objective of this study was to develop and validate a risk prediction model for the prediction of unfavorable treatment outcomes among MDR-TB patients in North-West Ethiopia.

Methods

We used MDR-TB data collected from the University of Gondar and Debre Markos referral hospitals. A retrospective follow-up study was conducted and a total of 517 patients were included in the study. STATA version 16 statistical software and R version 4.0.5 were used for the analysis. Descriptive statistics were carried out. A multivariable model was fitted using all potent predictors selected by the lasso regression method. A simplified risk prediction model (nomogram) was developed based on the binomial logit-based model, and its performance was described by assessing its discriminatory power and calibration. Finally, decision curve analysis (DCA) was done to evaluate the clinical and public health impact of the developed model.

Results

The developed nomogram comprised six predictors: baseline anemia, major adverse event, comorbidity, age, marital status, and treatment supporter. The model has a discriminatory power of 0.753 (95% CI: 0.708, 0.798) and calibration test of (P-value = 0.695). It was internally validated by bootstrapping method, and it has a relatively corrected discrimination performance (AUC = 0.744, 95CI: 0.699, 0.788). The optimism coefficient was found to be 0.009. The decision curve analysis showed the net benefit of the model as threshold probabilities varied.

Conclusion

The developed nomogram can be used for individualized prediction of unfavorable treatment outcomes in MDR-TB patients for it has a satisfactory level of accuracy and good calibration. The model is clinically interpretable and was found to have added benefits in clinical practice.

Intratumoral Budding in Pretreatment Biopsies, among Tumor Microenvironmental Components, Can Predict Prognosis and Neoadjuvant Therapy Response in Colorectal Adenocarcinoma

Background and Objectives: The prediction of the prognosis and effect of neoadjuvant therapy is vital for patients with advanced or unresectable colorectal carcinoma (CRC). Materials and Methods: We investigated several tumor microenvironment factors, such as intratumoral budding (ITB), desmoplastic reaction (DR), and Klintrup–Mäkinen (KM) inflammation grade, and the tumor–stroma ratio (TSR) in pretreatment biopsy samples (PBSs) collected from patients with advanced or unresectable CRC. A total of 85 patients with 74 rectal carcinomas and 11 colon cancers treated at our hospital were enrolled; 66 patients had curative surgery and 19 patients received palliative treatment. Results: High-grade ITB was associated with recurrence (p = 0.002), death (p = 0.034), and cancer-specific death (p = 0.034). Immature DR was associated with a higher grade of clinical tumor-node-metastasis stage (cTNM) (p = 0.045), cN category (p = 0.045), and cM category (p = 0.046). The KM grade and TSR were not related to any clinicopathological factors. High-grade ITB had a significant relationship with tumor regression in patients who received curative surgery (p = 0.049). Conclusions: High-grade ITB in PBSs is a potential unfavorable prognostic factor for patients with advanced CRC. Immature DR, TSR, and KM grade could not predict prognosis or therapy response in PBSs.

Non-invasive evaluation of NAFLD and the contribution of genes: an MRI-PDFF-based cross-sectional study

OBJECTIVE

To investigate the clinical, laboratory and genetic features of NAFLD patients based on MRI-PDFF in China.

DESIGN

Patients with high ALT and with a diagnosis of fatty liver were included in this cross-sectional study. Fasting blood was collected to test biomarkers and SNPs. A total of 266 patients underwent MRI-PDFF and FibroScan examinations, and 38 underwent liver biopsy. Diagnostic models (decision tree, LASSO, and elastic net) were developed based on the diagnosis from MRI-PDFF reports.

RESULTS

Approximately, 1/3 of the patients were found to have NASH and fibrosis. After quantifying liver steatosis by MRI-PDFF (healthy: n = 47; mild NAFLD: n = 136; moderate/severe NAFLD: n = 83; liver fat content (LFC): 3.6% vs. 8.7% vs. 19.0%), most biomarkers showed significant differences among the three groups, and patients without obesity were found to have a similar LFC as those with obesity (11.1% vs. 12.3%). Models including biomarkers showed strong diagnostic ability (accuracy: 0.80-0.91). Variant alleles of PNPLA3, HSD17B13 and MBOAT7 were identified as genetic risk factors causing higher LFC (8.7% vs. 12.3%; 11.0% vs. 14.5%; 8.5% vs. 10.2%, p < 0.05); those with the UQCC1 rs878639 variant allele showed lower LFC (10.4% vs. 8.4%; OR = 0.58, p < 0.05). Patients with more risk alleles had higher LFCs (8.1% vs. 10.7% vs. 11.6% vs. 14.5%).

CONCLUSIONS

Based on MRI-PDFF, a combination of several specific biomarkers can accurately predict disease status. When the effects of genes on liver steatosis were first quantified by MRI-PDFF, the UQCC1 rs878639 G allele was identified as a protective factor, and the MBOAT7 T allele was identified as a risk only among nonobese individuals.

Methodological Challenges in Predicting Periprosthetic Joint Infection Treatment Outcomes: A Narrative Review

The management of periprosthetic joint infection (PJI) generally requires both surgical intervention and targeted antimicrobial therapy. Decisions regarding surgical management–whether it be irrigation and debridement, one-stage revision, or two-stage revision–must take into consideration an array of factors. These include the timing and duration of symptoms, clinical characteristics of the patient, and antimicrobial susceptibilities of the microorganism(s) involved. Moreover, decisions relating to surgical management must consider clinical factors associated with the health of the patient, alongside the patient's preferences. These decisions are further complicated by concerns beyond mere eradication of the infection, such as the level of improvement in quality of life related to management strategies. To better understand the probability of successful surgical treatment of a PJI, several predictive tools have been developed over the past decade. This narrative review provides an overview of available clinical prediction models that aim to guide treatment decisions for patients with periprosthetic joint infection, and highlights key challenges to reliably implementing these tools in clinical practice.