Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement

Development and external validation of a nomogram for the early prediction of acute kidney injury in septic patients: a multicenter retrospective clinical study

Background and purpose: Acute kidney injury (AKI) is a common serious complication in sepsis patients with a high mortality rate. This study aimed to develop and validate a predictive model for sepsis associated acute kidney injury (SA-AKI). Methods: In our study, we retrospectively constructed a development cohort comprising 733 septic patients admitted to eight Grade-A tertiary hospitals in Shanghai from January 2021 to October 2022. Additionally, we established an external validation cohort consisting of 336 septic patients admitted to our hospital from January 2017 to December 2019. Risk predictors were selected by LASSO regression, and a corresponding nomogram was constructed. We evaluated the model's discrimination, precision and clinical benefit through receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA) and clinical impact curves (CIC) in both internal and external validation. Results: AKI incidence was 53.2% in the development cohort and 48.2% in the external validation cohort. The model included five independent indicators: chronic kidney disease stages 1 to 3, blood urea nitrogen, procalcitonin, D-dimer and creatine kinase isoenzyme. The AUC of the model in the development and validation cohorts was 0.914 (95% CI, 0.894-0.934) and 0.923 (95% CI, 0.895-0.952), respectively. The calibration plot, DCA, and CIC demonstrated the model's favorable clinical applicability. Conclusion: We developed and validated a robust nomogram model, which might identify patients at risk of SA-AKI and promising for clinical applications.

A joint physics and radiobiology DREAM team vision - Towards better response prediction models to advance radiotherapy

Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team's consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines.

Web-based artificial intelligence to predict cognitive impairment following stroke: A multicenter study

BACKGROUND AND PURPOSE

Post-stroke cognitive impairment (PSCI) is highly prevalent in modern society. However, there is limited study implying an accurate and explainable machine learning model to predict PSCI. The aim of this study is to develop and validate a web-based artificial intelligence (AI) tool for predicting PSCI.

METHODS

The retrospective cohort study design was conducted to develop and validate a web-based prediction model. Adults who experienced a stroke between January 1, 2004, and September 30, 2017, were enrolled, and patients with PSCI were followed up from the stroke index date until their last follow-up. The model's performance metrics, including accuracy, area under the curve (AUC), recall, precision, and F1 score, were compared.

RESULTS

A total of 3209 stroke patients were included in the study. The model demonstrated an accuracy of 0.8793, AUC of 0.9200, recall of 0.6332, precision of 0.9664, and F1 score of 0.7651. In the external validation phase, the accuracy improved to 0.9039, AUC to 0.9094, recall to 0.7457, precision to 0.9168, and F1 score to 0.8224. The final model can be accessed at https://psci-calculator.my.id/.

CONCLUSION

Our results are able to produce a user-friendly interface that is useful for health practitioners to perform early prediction on PSCI. These findings also suggest that the provided AI model is reliable and can serve as a roadmap for future studies using AI models in a clinical setting.

Practical guide to building machine learning-based clinical prediction models using imbalanced datasets

Clinical prediction models often aim to predict rare, high-risk events, but building such models requires robust understanding of imbalance datasets and their unique study design considerations. This practical guide highlights foundational prediction model principles for surgeon-data scientists and readers who encounter clinical prediction models, from feature engineering and algorithm selection strategies to model evaluation and design techniques specific to imbalanced datasets. We walk through a clinical example using readable code to highlight important considerations and common pitfalls in developing machine learning-based prediction models. We hope this practical guide facilitates developing and critically appraising robust clinical prediction models for the surgical community.

Parallel CNN-Deep Learning Clinical-Imaging Signature for Assessing Pathologic Grade and Prognosis of Soft Tissue Sarcoma Patients

BACKGROUND

Traditional biopsies pose risks and may not accurately reflect soft tissue sarcoma (STS) heterogeneity. MRI provides a noninvasive, comprehensive alternative.

PURPOSE

To assess the diagnostic accuracy of histological grading and prognosis in STS patients when integrating clinical-imaging parameters with deep learning (DL) features from preoperative MR images.

STUDY TYPE

Retrospective/prospective.

POPULATION

354 pathologically confirmed STS patients (226 low-grade, 128 high-grade) from three hospitals and the Cancer Imaging Archive (TCIA), divided into training (n = 185), external test (n = 125), and TCIA cohorts (n = 44). 12 patients (6 low-grade, 6 high-grade) were enrolled into prospective validation cohort.

FIELD STRENGTH/SEQUENCE

1.5 T and 3.0 T/Unenhanced T1-weighted and fat-suppressed-T2-weighted.

ASSESSMENT

DL features were extracted from MR images using a parallel ResNet-18 model to construct DL signature. Clinical-imaging characteristics included age, gender, tumor-node-metastasis stage and MRI semantic features (depth, number, heterogeneity at T1WI/FS-T2WI, necrosis, and peritumoral edema). Logistic regression analysis identified significant risk factors for the clinical model. A DL clinical-imaging signature (DLCS) was constructed by incorporating DL signature with risk factors, evaluated for risk stratification, and assessed for progression-free survival (PFS) in retrospective cohorts, with an average follow-up of 23 ± 22 months.

STATISTICAL TESTS

Logistic regression, Cox regression, Kaplan-Meier curves, log-rank test, area under the receiver operating characteristic curve (AUC)，and decision curve analysis. A P-value <0.05 was considered significant.

RESULTS

The AUC values for DLCS in the external test, TCIA, and prospective test cohorts (0.834, 0.838, 0.819) were superior to clinical model (0.662, 0.685, 0.694). Decision curve analysis showed that the DLCS model provided greater clinical net benefit over the DL and clinical models. Also, the DLCS model was able to risk-stratify patients and assess PFS.

DATA CONCLUSION

The DLCS exhibited strong capabilities in histological grading and prognosis assessment for STS patients, and may have potential to aid in the formulation of personalized treatment plans.

LEVEL OF EVIDENCE: 4

TECHNICAL EFFICACY

Stage 2.

First experiences with machine learning predictions of accelerated declining eGFR slope of living kidney donors 3 years after donation

BACKGROUND

Living kidney donors are screened pre-donation to estimate the risk of end-stage kidney disease (ESKD). We evaluate Machine Learning (ML) to predict the progression of kidney function deterioration over time using the estimated GFR (eGFR) slope as the target variable.

METHODS

We included 238 living kidney donors who underwent donor nephrectomy. We divided the dataset based on the eGFR slope in the third follow-up year, resulting in 185 donors with an average eGFR slope and 53 donors with an accelerated declining eGFR-slope. We trained three Machine Learning-models (Random Forest [RF], Extreme Gradient Boosting [XG], Support Vector Machine [SVM]) and Logistic Regression (LR) for predictions. Predefined data subsets served for training to explore whether parameters of an ESKD risk score alone suffice or additional clinical and time-zero biopsy parameters enhance predictions. Machine learning-driven feature selection identified the best predictive parameters.

RESULTS

None of the four models classified the eGFR slope with an AUC greater than 0.6 or an F1 score surpassing 0.41 despite training on different data subsets. Following machine learning-driven feature selection and subsequent retraining on these selected features, random forest and extreme gradient boosting outperformed other models, achieving an AUC of 0.66 and an F1 score of 0.44. After feature selection, two predictive donor attributes consistently appeared in all models: smoking-related features and glomerulitis of the Banff Lesion Score.

CONCLUSIONS

Training machine learning-models with distinct predefined data subsets yielded unsatisfactory results. However, the efficacy of random forest and extreme gradient boosting improved when trained exclusively with machine learning-driven selected features, suggesting that the quality, rather than the quantity, of features is crucial for machine learning-model performance. This study offers insights into the application of emerging machine learning-techniques for the screening of living kidney donors.

Assessing the generalisability of the psychosis metabolic risk calculator (PsyMetRiC) for young people with first-episode psychosis with validation in a Hong Kong Chinese Han population: a 4-year follow-up study

Background

Metabolic syndrome (MetS) is common following first-episode psychosis (FEP), contributing to substantial morbidity and mortality. The Psychosis Metabolic Risk Calculator (PsyMetRiC), a risk prediction algorithm for MetS following a FEP diagnosis, was developed in the United Kingdom and has been validated in other European populations. However, the predictive accuracy of PsyMetRiC in Chinese populations is unknown.

Methods

FEP patients aged 15-35 y, first presented to the Early Assessment Service for Young People with Early Psychosis (EASY) Programme in Hong Kong (HK) between 2012 and 2021 were included. A binary MetS outcome was determined based on the latest available follow-up clinical information between 1 and 12 years after baseline assessment. The PsyMetRiC Full and Partial algorithms were assessed for discrimination, calibration and clinical utility in the HK sample, and logistic calibration was conducted to account for population differences. Sensitivity analysis was performed in patients aged >35 years and using Chinese MetS criteria.

Findings

The main analysis included 416 FEP patients (mean age = 23.8 y, male sex = 40.4%, 22.4% MetS prevalence at follow-up). PsyMetRiC showed adequate discriminative performance (full-model C = 0.76, 95% C.I. = 0.69-0.81; partial-model: C = 0.73, 95% C.I. = 0.65-0.8). Systematic risk underestimation in both models was corrected using logistic calibration to refine PsyMetRiC for HK Chinese FEP population (PsyMetRiC-HK). PsyMetRiC-HK provided a greater net benefit than competing strategies. Results remained robust with a Chinese MetS definition, but worse for the older age group.

Interpretation

With good predictive performance for incident MetS, PsyMetRiC-HK presents a step forward for personalized preventative strategies of cardiometabolic morbidity and mortality in young Hong Kong Chinese FEP patients.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Smoking, respiratory symptoms, lung function and life expectancy: A longitudinal study of ageing

BACKGROUND AND OBJECTIVE

Prognostic indices have been developed to predict various outcomes, including mortality. These indices and hazard ratios may be difficult for patients to understand. We investigated the association between smoking, respiratory symptoms and lung function with remaining life expectancy (LE) in older adults.

METHODS

Data were from the 2004/05 English Longitudinal Study of Ageing (ELSA) (n = 8930), participants aged ≥50-years, with mortality data until 2012. Respiratory symptoms included were chronic phlegm and shortness of breath (SOB). The association between smoking, respiratory symptoms and FEV1/FVC, and remaining LE was estimated using a parametric survival function and adjusted for covariates including age at baseline and sex.

RESULTS

The extent to which symptoms and FEV1/FVC predicted differences in remaining LE varied by smoking. Compared to asymptomatic never smokers with normal lung function (the reference group), in never smokers, only those with SOB had a significant reduction in remaining LE. In former and current smokers, those with respiratory symptoms had significantly lower remaining LE compared to the reference group if they had FEV1/FVC <0.70 compared to those with FEV1/FVC ≥0.70. Males aged 50-years, current smokers with SOB and FEV1/FVC <0.70, had a remaining LE of 19.2 (95%CI: 16.5-22.2) years, a decrease of 8.1 (5.3-10.8) years, compared to the reference group.

CONCLUSION

Smoking, respiratory symptoms and FEV1/FVC are strongly associated with remaining LE in older people. The use of remaining LE to communicate mortality risk to patients needs further investigation.

Clinicopathological Predictors of Positive Resection Margins in Breast-Conserving Surgery

BACKGROUND

Ductal carcinoma in situ (DCIS) is associated with risk of positive resection margins following breast-conserving surgery (BCS) and subsequent reoperation. Prior reports grossly underestimate the risk of margin positivity with IBC containing a DCIS component (IBC + DCIS) due to patient-level rather than margin-level analysis.

OBJECTIVE

The aim of this study was to delineate the relative risk of IBC + DCIS compared with pure IBC (without a DCIS component) on margin positivity through detailed margin-level interrogation.

METHODS

A single institution, retrospective, observational cohort study was conducted in which pathology databases were evaluated to identify patients who underwent BCS over 5 years (2014-2019). Margin-level interrogation included granular detail into the extent, pathological subtype and grade of disease at each resection margin. Predictors of a positive margin were computed using multivariate regression analysis.

RESULTS

Clinicopathological details were examined from 5454 margins from 909 women. The relative risk of a positive margin with IBC + DCIS versus pure IBC was 8.76 (95% confidence interval [CI] 6.64-11.56) applying UK Association of Breast Surgery guidelines, and 8.44 (95% CI 6.57-10.84) applying the Society of Surgical Oncology/American Society for Radiation Oncology guidelines. Independent predictors of margin positivity included younger patient age (0.033, 95% CI 0.006-0.060), lower specimen weight (0.045, 95% CI 0.020-0.069), multifocality (0.256, 95% CI 0.137-0.376), lymphovascular invasion (0.138, 95% CI 0.068-0.208) and comedonecrosis (0.113, 95% CI 0.040-0.185).

CONCLUSIONS

Compared with pure IBC, the relative risk of a positive margin with IBC + DCIS is approximately ninefold, significantly higher than prior estimates. This margin-level methodology is believed to represent the impact of DCIS more accurately on margin positivity in IBC.

Deep learning predicts postoperative opioids refills in a multi-institutional cohort of surgical patients

BACKGROUND

To combat the opioid epidemic, several strategies were implemented to limit the unnecessary prescription of opioids in the postoperative period. However, this leaves a subset of patients who genuinely require additional opioids with inadequate pain control. Deep learning models are powerful tools with great potential of optimizing health care delivery through a patient-centered focus. We sought to investigate whether deep learning models can be used to predict patients who would require additional opioid prescription refills in the postoperative period after elective surgery.

METHODS

This is a retrospective study of patients who received elective surgical intervention at the Mayo Clinic. Adult English-speaking patients ≥18 years old, who underwent an elective surgical procedure between 2013 and 2019, were eligible for inclusion. Machine learning models, including deep learning, random forest, and eXtreme Gradient Boosting, were designed to predict patients who require opioid refills after discharge from hospital.

RESULTS

A total of 9,731 patients with mean age of 62.1 years (51.4% female) were included in the study. Deep learning and random forest models predicted patients who required opioid refills with high accuracy, 0.79 ± 0.07 and 0.78 ± 0.08, respectively. Procedure performed, highest pain score recorded during hospitalization, and total oral morphine milligram equivalents prescribed at discharge were the top 3 predictors for requiring opioid refills after discharge.

CONCLUSION

Deep learning models can be used to predict patients who require postoperative opioid prescription refills with high accuracy. Other machine learning models, such as random forest, can perform equal to deep learning, increasing the applicability of machine learning for combating the opioid epidemic.

Prognosis prediction models for post-stroke depression: a protocol for systematic review, meta-analysis, and critical appraisal

INTRODUCTION

Post-stroke depression (PSD) is a prevalent complication that has been shown to have a negative impact on rehabilitation outcomes and quality of life and poses a significant risk for suicidal intention. However, models for discriminating and predicting PSD in stroke survivors for effective secondary prevention strategies are inadequate as the pathogenesis of PSD remains unknown. Prognostic prediction models that exhibit greater rule-in capacity have the potential to mitigate the issue of underdiagnosis and undertreatment of PSD. Thus, the planned study aims to systematically review and critically evaluate published studies on prognostic prediction models for PSD.

METHODS AND ANALYSIS

A systematic literature search will be conducted in PubMed and Embase through Ovid. Two reviewers will complete study screening, data extraction, and quality assessment utilizing appropriate tools. Qualitative data on the characteristics of the included studies, methodological quality, and the appraisal of the clinical applicability of models will be summarized in the form of narrative comments and tables or figures. The predictive performance of the same model involving multiple studies will be synthesized with a random effects meta-analysis model or meta-regression, taking into account heterogeneity.

ETHICS AND DISSEMINATION

Ethical approval is considered not applicable for this systematic review. Findings will be shared through dissemination at academic conferences and/or publication in peer-reviewed academic journals.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42023388548.

Machine learning-based preoperative analytics for the prediction of anastomotic leakage in colorectal surgery: a swiss pilot study

BACKGROUND

Anastomotic leakage (AL), a severe complication following colorectal surgery, arises from defects at the anastomosis site. This study evaluates the feasibility of predicting AL using machine learning (ML) algorithms based on preoperative data.

METHODS

We retrospectively analyzed data including 21 predictors from patients undergoing colorectal surgery with bowel anastomosis at four Swiss hospitals. Several ML algorithms were applied for binary classification into AL or non-AL groups, utilizing a five-fold cross-validation strategy with a 90% training and 10% validation split. Additionally, a holdout test set from an external hospital was employed to assess the models' robustness in external validation.

RESULTS

Among 1244 patients, 112 (9.0%) suffered from AL. The Random Forest model showed an AUC-ROC of 0.78 (SD: ± 0.01) on the internal test set, which significantly decreased to 0.60 (SD: ± 0.05) on the external holdout test set comprising 198 patients, including 7 (3.5%) with AL. Conversely, the Logistic Regression model demonstrated more consistent AUC-ROC values of 0.69 (SD: ± 0.01) on the internal set and 0.61 (SD: ± 0.05) on the external set. Accuracy measures for Random Forest were 0.82 (SD: ± 0.04) internally and 0.87 (SD: ± 0.08) externally, while Logistic Regression achieved accuracies of 0.81 (SD: ± 0.10) and 0.88 (SD: ± 0.15). F1 Scores for Random Forest moved from 0.58 (SD: ± 0.03) internally to 0.51 (SD: ± 0.03) externally, with Logistic Regression maintaining more stable scores of 0.53 (SD: ± 0.04) and 0.51 (SD: ± 0.02).

CONCLUSION

In this pilot study, we evaluated ML-based prediction models for AL post-colorectal surgery and identified ten patient-related risk factors associated with AL. Highlighting the need for multicenter data, external validation, and larger sample sizes, our findings emphasize the potential of ML in enhancing surgical outcomes and inform future development of a web-based application for broader clinical use.

Dosimetric and NTCP analyses for selecting parotid gland cancer patients for proton therapy

PURPOSE/OBJECTIVE

To perform a dosimetric and a normal tissue complication probability (NTCP) comparison between intensity modulated proton therapy and photon volumetric modulated arc therapy in a cohort of patients with parotid gland cancers in a post-operative or radical setting.

MATERIALS AND METHODS

From May 2011 to September 2021, 37 parotid gland cancers patients treated at two institutions were eligible. Inclusion criteria were as follows: patients aged ⩾ 18 years, diagnosis of parotid gland cancers candidate for postoperative radiotherapy or definitive radiotherapy, presence of written informed consent for the use of anonymous data for research purposes. Organs at risk (OARs) were retrospectively contoured. Target coverage goal was defined as D95 > 98%. Six NTCP models were selected. NTCP profiles were calculated for each patient using an internally-developed Python script in RayStation TPS. Average differences in NTCP between photon and proton plans were tested for significance with a two-sided Wilcoxon signed-rank test.

RESULTS

Seventy-four plans were generated. A lower Dmean to the majority of organs at risk (inner ear, cochlea, oral cavity, pharyngeal constrictor muscles, contralateral parotid and submandibular gland) was obtained with intensity modulated proton therapy vs volumetric modulated arc therapy with statistical significance (p < .05). Ten (27%) patients had a difference in NTCP (photon vs proton plans) greater than 10% for hearing loss and tinnitus: among them, seven qualified for both endpoints, two patients for hearing loss only, and one for tinnitus.

CONCLUSIONS

In the current study, nearly one-third of patients resulted eligible for proton therapy and they were the most likely to benefit in terms of prevention of hearing loss and tinnitus.

Prediction of survival after neoadjuvant therapy in locally advanced rectal cancer - a retrospective analysis

Purpose

The aim of this retrospective analysis was to determine if the response to preoperative radio(chemo)therapy is predictive for survival among patients with locally advanced rectal cancer and may act as a potential surrogate endpoint for disease free survival and overall survival.

Results

Eight hundred seventy-eight patients from five centers were analyzed. There were 304 women and 574 men; the median age was 64.7 years. 77.6% and 22.4% of patients received neoadjuvant radiochemotherapy or short-course radiotherapy, resulting in a pathological complete response in 7.3%. T-downstaging and N-downstaging occurred in 50.5% and 37% of patients after neoadjuvant therapy. In patients with T-downstaging, the 10-year DFS and 10-year OS were 64.8% and 66.8% compared to 37.1% and 45.9% in patients without T-downstaging. N-downstaging resulted in 10-year DFS and 10-year OS in 56.2% and 62.5% compared to 47.3% and 52.3% without N-downstaging. Based on routinely evaluated clinical parameters, an absolute risk prediction calculator was generated for 5-year disease-free survival, and 5-year overall survival.

Conclusion

T-downstaging and N-downstaging after neoadjuvant radiochemotherapy or short-course radiotherapy resulted in better DFS and OS compared to patients without response. Based on clinical parameters, 5-year DFS, and 5-year OS can be predicted using a prediction calculator.

Prediction of carbapenem-resistant gram-negative bacterial bloodstream infection in intensive care unit based on machine learning

BACKGROUND

Predicting whether Carbapenem-Resistant Gram-Negative Bacterial (CRGNB) cause bloodstream infection when giving advice may guide the use of antibiotics because it takes 2-5 days conventionally to return the results from doctor's order.

METHODS

It is a regional multi-center retrospective study in which patients with suspected bloodstream infections were divided into a positive and negative culture group. According to the positive results, patients were divided into the CRGNB group and other groups. We used the machine learning algorithm to predict whether the blood culture was positive and whether the pathogen was CRGNB once giving the order of blood culture.

RESULTS

There were 952 patients with positive blood cultures, 418 patients in the CRGNB group, 534 in the non-CRGNB group, and 1422 with negative blood cultures. Mechanical ventilation, invasive catheterization, and carbapenem use history were the main high-risk factors for CRGNB bloodstream infection. The random forest model has the best prediction ability, with AUROC being 0.86, followed by the XGBoost prediction model in bloodstream infection prediction. In the CRGNB prediction model analysis, the SVM and random forest model have higher area under the receiver operating characteristic curves, which are 0.88 and 0.87, respectively.

CONCLUSIONS

The machine learning algorithm can accurately predict the occurrence of ICU-acquired bloodstream infection and identify whether CRGNB causes it once giving the order of blood culture.

Proteomic Biomarkers for the Prediction of Transition to Psychosis in Individuals at Clinical High Risk: A Multi-cohort Model Development Study

Psychosis risk prediction is one of the leading challenges in psychiatry. Previous investigations have suggested that plasma proteomic data may be useful in accurately predicting transition to psychosis in individuals at clinical high risk (CHR). We hypothesized that an a priori-specified proteomic prediction model would have strong predictive accuracy for psychosis risk and aimed to replicate longitudinal associations between plasma proteins and transition to psychosis. This study used plasma samples from participants in 3 CHR cohorts: the North American Prodrome Longitudinal Studies 2 and 3, and the NEURAPRO randomized control trial (total n = 754). Plasma proteomic data were quantified using mass spectrometry. The primary outcome was transition to psychosis over the study follow-up period. Logistic regression models were internally validated, and optimism-corrected performance metrics derived with a bootstrap procedure. In the overall sample of CHR participants (age: 18.5, SD: 3.9; 51.9% male), 20.4% (n = 154) developed psychosis within 4.4 years. The a priori-specified model showed poor risk-prediction accuracy for the development of psychosis (C-statistic: 0.51 [95% CI: 0.50, 0.59], calibration slope: 0.45). At a group level, Complement C8B, C4B, C5, and leucine-rich α-2 glycoprotein 1 (LRG1) were associated with transition to psychosis but did not surpass correction for multiple comparisons. This study did not confirm the findings from a previous proteomic prediction model of transition from CHR to psychosis. Certain complement proteins may be weakly associated with transition at a group level. Previous findings, derived from small samples, should be interpreted with caution.

TEMPORARY REMOVAL: A novel scoring system to predict short-term mortality after living donor liver transplantation for acute liver failure

The publisher regrets that this article has been temporarily removed. A replacement will appear as soon as possible in which the reason for the removal of the article will be specified, or the article will be reinstated. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies/article-withdrawal.

Validation of a Risk Prediction Equation for Incident Chronic Kidney Disease in a Hypertensive Non-Diabetes Cohort in Singapore Primary Care Patients

BACKGROUND

Accurate identification of individuals at risk of developing chronic kidney disease (CKD) may improve clinical care. Nelson et al. developed prediction equations to estimate the risk of incident eGFR of less than 60 mL/min/1.73 m2 in diabetic and non-diabetes patients using data from 34 multinational cohorts. We aim to validate the non-diabetes equation in our local multi-ethnic cohort and develop further prediction models.

METHODS

Demographics, clinical and laboratory data of hypertensive non-diabetes patients with baseline eGFR ≥60 mL/min/1.73 m2 on follow-up with primary care clinics between 2010 and 2015 were collected. Follow-up was 5 years from entry to study. We validated Nelson's equation and developed our own model which we subsequently validated. The developmental cohort included patients between 2010 and 2014 while the validation cohort included patients in 2015. Variables included age, sex, eGFR, history of cardiovascular disease, ever smoker, body mass index, albuminuria, cholesterol, and treatment. Primary outcome was incident eGFR <60/min/1.73 m2 within 5 years. Model performance was evaluated by C-statistics and calibration was assessed.

RESULTS

In the developmental cohort of 27,800 patients, 2823 (10.2%) developed the outcome during a mean follow-up of 4.4 years while 638 (12.8%) patients developed the outcome in the validation cohort of 4,994 patients. Applicability of Nelson's equation was limited by missing albuminuria, absence of black race, and exclusion of non-hypertensive patients in our cohort. Nonetheless, the modified Nelson's model demonstrated C-statistic of 0.85 (95% CI: 0.84-0.86). The C-statistic of our bespoke model was 0.85 (0.85-0.86) and 0.87 (0.85-0.88) for the developmental cohort and validation cohort, respectively. Calibration was suboptimal as the predicted risk exceeded the observed risk.

CONCLUSIONS

The modified Nelson's equation and our locally derived novel model demonstrated high discrimination. Both models may potentially be used in predicting risk of CKD in hypertensive patients who are managed in primary care, allowing for early interventions in high-risk population.

Gender potentially affects early postoperative hyponatremia in pituitary adenoma: XGBoost-based predictive modeling

Purpose

The occurrence of hyponatremia is a prevalent complication following transnasal transsphenoidal surgery for pituitary adenoma surgery, which adversely affects patient prognosis, hospitalization duration, and rehospitalization risk. The primary objective of this study is to strengthen the correlation between clinical factors associated with pituitary adenoma and postoperative hyponatremia. Additionally, the study aims to develop a predictive model for postoperative hyponatremia in patients with pituitary adenoma, with the ultimate goal of establishing a basis for reducing the occurrence of postoperative hyponatremia following surgical interventions.

Methods

The chi-square test or Fisher test was employed for nominal data, while the t-test or Mann-Whitney test was utilized for continuous data analysis. In cases where the data exhibited statistical differences, binary logistic analysis was conducted to examine the risk and protective factors associated with postoperative hyponatremia. XGBoost was employed to construct predictive models for hyponatremia in this study. The patients were partitioned into training and test sets, and the most suitable parameters were determined through five-fold cross-validation and subsequently utilized for training on the training set. The discriminatory capability was assessed on the internal validation set.

Results and conclusions

Out of the total 280 patients included in this investigation, 82 patients experienced early postoperative hyponatremia. Among these individuals, male gender (P = 0.02, odds ratio = 1.98) was identified as a risk factor for early postoperative hyponatremia, while preoperative chloride levels (P = 0.021, odds ratio = 0.866) and surgery time (P = 0.039, odds ratio = 0.990) were identified as protective factors against postoperative hyponatremia. The XGBoost model exhibited a sensitivity of 94.2%, a specificity of 61.5%, a positive predictive value of 51.6%, a negative predictive value of 96%, and identified male gender, preoperative sodium, and preoperative cortisol as the most significant predictors. Our findings indicate that gender may have influence in the development of early postoperative hyponatremia in patients with pituitary adenomas.

Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures

Using clustering analysis for early vital signs, unique patient phenotypes with distinct pathophysiological signatures and clinical outcomes may be revealed and support early clinical decision-making. Phenotyping using early vital signs has proven challenging, as vital signs are typically sampled sporadically. We proposed a novel, deep temporal interpolation and clustering network to simultaneously extract latent representations from irregularly sampled vital signs and derive phenotypes. Four distinct clusters were identified. Phenotype A (18%) had the greatest prevalence of comorbid disease with increased prevalence of prolonged respiratory insufficiency, acute kidney injury, sepsis, and long-term (3-year) mortality. Phenotypes B (33%) and C (31%) had a diffuse pattern of mild organ dysfunction. Phenotype B's favorable short-term clinical outcomes were tempered by the second highest rate of long-term mortality. Phenotype C had favorable clinical outcomes. Phenotype D (17%) exhibited early and persistent hypotension, high incidence of early surgery, and substantial biomarker incidence of inflammation. Despite early and severe illness, phenotype D had the second lowest long-term mortality. After comparing the sequential organ failure assessment scores, the clustering results did not simply provide a recapitulation of previous acuity assessments. This tool may impact triage decisions and have significant implications for clinical decision-support under time constraints and uncertainty.

Lymph node metastases and recurrence in pT1 colorectal cancer: Prediction with the International Budding Consortium Score-A retrospective, multi-centric study

BACKGROUND

The International Collaboration on Cancer Reporting proposes histological tumour type, lymphovascular invasion, tumour grade, perineural invasion, extent, and dimensions of invasion as risk factors for lymph node metastases and tumour progression in completely endoscopically resected pT1 colorectal cancer (CRC).

OBJECTIVE

The aim of the study was to propose a predictive and reliable score to optimise the clinical management of endoscopically resected pT1 CRC patients.

METHODS

This multi-centric, retrospective International Budding Consortium (IBC) study included an international pT1 CRC cohort of 565 patients. All cases were reviewed by eight expert gastrointestinal pathologists. All risk factors were reported according to international guidelines. Tumour budding and immune response (CD8+ T-cells) were assessed with automated models using artificial intelligence. We used the information on risk factors and least absolute shrinkage and selection operator logistic regression to develop a prediction model and generate a score to predict the occurrence of lymph node metastasis or cancer recurrence.

RESULTS

The IBC prediction score included the following parameters: lymphovascular invasion, tumour buds, infiltration depth and tumour grade. The score has an acceptable discrimination power (area under the curve of 0.68 [95% confidence intervals (CI) 0.61-0.75]; 0.64 [95% CI 0.57-0.71] after internal validation). At a cut-off of 6.8 points to discriminate high-and low-risk patients, the score had a sensitivity and specificity of 0.9 [95% CI 0.8-0.95] and 0.26 [95% 0.22, 0.3], respectively.

CONCLUSION

The IBC score is based on well-established risk factors and is a promising tool with clinical utility to support the management of pT1 CRC patients.

A Machine Learning Model Demonstrates Excellent Performance in Predicting Subscapularis Tears Based on Pre-Operative Imaging Parameters Alone

PURPOSE

To develop a machine learning model capable of identifying subscapularis tears before surgery based on imaging and physical examination findings.

METHODS

Between 2010 and 2020, 202 consecutive shoulders underwent arthroscopic rotator cuff repair by a single surgeon. Patient demographics, physical examination findings (including range of motion, weakness with internal rotation, lift/push-off test, belly press test, and bear hug test), and imaging (including direct and indirect signs of tearing, biceps status, fatty atrophy, cystic changes, and other similar findings) were included for model creation.

RESULTS

Sixty percent of the shoulders had partial or full thickness tears of the subscapularis verified during surgery (83% of these were upper third). Using only preoperative imaging-related parameters, the XGBoost model demonstrated excellent performance at predicting subscapularis tears (c-statistic, 0.84; accuracy, 0.85; F1 score, 0.87). The top 5 features included direct signs related to the presence of tearing as evidenced on magnetic resonance imaging (MRI) (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology.

CONCLUSIONS

In this study, machine learning was successful in predicting subscapularis tears by MRI alone in 85% of patients, and this accuracy did not decrease by isolating the model to the top features. The top five features included direct signs related to the presence of tearing as evidenced on MRI (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. Last, in advanced modeling, the addition of physical examination or patient characteristics did not make a significant difference in the predictive ability of this model.

LEVEL OF EVIDENCE

Level III, diagnostic case-control study.

Development and Validation of the Pharmacological Statin-Associated Muscle Symptoms Risk Stratification Score Using Electronic Health Record Data

Statin-associated muscle symptoms (SAMS) can lead to statin nonadherence. This paper aims to develop a pharmacological SAMS risk stratification (PSAMS-RS) score using a previously developed PSAMS phenotyping algorithm that distinguishes objective vs. nocebo SAMS using electronic health record (EHR) data. Using our PSAMS phenotyping algorithm, SAMS cases and controls were identified from Minnesota Fairview EHR, with the statin user cohort divided into derivation (January 1, 2010, to December 31, 2018) and validation (January 1, 2019, to December 31, 2020) cohorts. A Least Absolute Shrinkage and Selection Operator regression model was applied to identify significant features for PSAMS. PSAMS-RS scores were calculated and the clinical utility of stratifying PSAMS risk was assessed by comparing hazard ratios (HRs) between fourth vs. first score quartiles. PSAMS cases were identified in 1.9% (310/16,128) of the derivation and 1.5% (64/4,182) of the validation cohorts. Sixteen out of 38 clinical features were determined to be significant predictors for PSAMS risk. Patients within the fourth quartile of the PSAMS scores had an over sevenfold (HR: 7.1, 95% confidence interval (CI): 4.03-12.45, derivation cohort) or sixfold (HR: 6.1, 95% CI: 2.15-17.45, validation cohort) higher hazard of developing PSAMS vs. those in their respective first quartile. The PSAMS-RS score is a simple tool to stratify patients' risk of developing PSAMS after statin initiation which could inform clinician-guided pre-emptive measures to prevent PSAMS-related statin nonadherence.

Deep learning on pre-procedural computed tomography and clinical data predicts outcome following stroke thrombectomy

BACKGROUND

Deep learning using clinical and imaging data may improve pre-treatment prognostication in ischemic stroke patients undergoing endovascular thrombectomy (EVT).

METHODS

Deep learning models were trained and tested on baseline clinical and imaging (CT head and CT angiography) data to predict 3-month functional outcomes in stroke patients who underwent EVT. Classical machine learning models (logistic regression and random forest classifiers) were constructed to compare their performance with the deep learning models. An external validation dataset was used to validate the models. The MR PREDICTS prognostic tool was tested on the external validation set, and its performance was compared with the deep learning and classical machine learning models.

RESULTS

A total of 975 patients (550 men; mean±SD age 67.5±15.1 years) were studied with 778 patients in the model development cohort and 197 in the external validation cohort. The deep learning model trained on baseline CT and clinical data, and the logistic regression model (clinical data alone) demonstrated the strongest discriminative abilities for 3-month functional outcome and were comparable (AUC 0.811 vs 0.817, Q=0.82). Both models exhibited superior prognostic performance than the other deep learning (CT head alone, CT head, and CT angiography) and MR PREDICTS models (all Q<0.05).

CONCLUSIONS

The discriminative performance of deep learning for predicting functional independence was comparable to logistic regression. Future studies should focus on whether incorporating procedural and post-procedural data significantly improves model performance.

Development of short forms for screening children's dental caries and urgent treatment needs using item response theory and machine learning methods

OBJECTIVES

Surveys can assist in screening oral diseases in populations to enhance the early detection of disease and intervention strategies for children in need. This paper aims to develop short forms of child-report and proxy-report survey screening instruments for active dental caries and urgent treatment needs in school-age children.

METHODS

This cross-sectional study recruited 497 distinct dyads of children aged 8-17 and their parents between 2015 to 2019 from 14 dental clinics and private practices in Los Angeles County. We evaluated responses to 88 child-reported and 64 proxy-reported oral health questions to select and calibrate short forms using Item Response Theory. Seven classical Machine Learning algorithms were employed to predict children's active caries and urgent treatment needs using the short forms together with family demographic variables. The candidate algorithms include CatBoost, Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes, Neural Network, Random Forest, and Support Vector Machine. Predictive performance was assessed using repeated 5-fold nested cross-validations.

RESULTS

We developed and calibrated four ten-item short forms. Naïve Bayes outperformed other algorithms with the highest median of cross-validated area under the ROC curve. The means of best testing sensitivities and specificities using both child-reported and proxy-reported responses were 0.84 and 0.30 for active caries, and 0.81 and 0.31 for urgent treatment needs respectively. Models incorporating both response types showed a slightly higher predictive accuracy than those relying on either child-reported or proxy-reported responses.

CONCLUSIONS

The combination of Item Response Theory and Machine Learning algorithms yielded potentially useful screening instruments for both active caries and urgent treatment needs of children. The survey screening approach is relatively cost-effective and convenient when dealing with oral health assessment in large populations. Future studies are needed to further leverage the customize and refine the instruments based on the estimated item characteristics for specific subgroups of the populations to enhance predictive accuracy.

Development of a predictive model for risk stratification of acute kidney injury in patients undergoing cytoreductive surgery with hyperthermic intraperitoneal chemotherapy

Acute kidney injury (AKI) following hyperthermic intraperitoneal chemotherapy (HIPEC) is common. Identifying patients at risk could have implications for surgical and anesthetic management. We aimed to develop a predictive model that could predict AKI based on patients' preoperative characteristics and intraperitoneal chemotherapy regimen. We retrospectively gathered data of adult patients undergoing HIPEC at our health system between November 2013 and April 2022. Next, we developed a model predicting postoperative AKI using multivariable logistic regression and calculated the performance of the model (area under the receiver operating characteristics curve [AUC]) via tenfold cross-validation. A total of 412 patients were included, of which 36 (8.7%) developed postoperative AKI. Based on our multivariable logistic regression model, multiple preoperative and intraoperative characteristics were associated with AKI. We included the total intraoperative cisplatin dose, body mass index, male sex, and preoperative hemoglobin level in the final model. The mean area under the receiver operating characteristics curve value was 0.82 (95% confidence interval 0.71-0.93). Our risk model predicted AKI with high accuracy in patients undergoing HIPEC in our institution. The external validity of our model should now be tested in independent and prospective patient cohorts.

Derivation of the Difficult Airway Physiological Score (DAPS) in adults undergoing endotracheal intubation in the emergency department

BACKGROUND

Prediction of serious outcomes among patients with physiological instability is crucial in airway management. In this study, we aim to develop a score to predict serious outcomes following intubation in critically ill adults with physiological instability by using clinical and laboratory parameters collected prior to intubation.

METHOD

This single-center analytical cross-sectional study was conducted in the Emergency Department from 2016 to 2020. The airway score was derived using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) methodology. To gauge model's performance, the train-test split technique was utilized. The discrete random number generation approach was used to divide the dataset into two groups: development (training) and validation (testing). The validation dataset's instances were used to calculate the final score, and its validity was measured using ROC analysis and area under the curve (AUC). By computing the Youden's J statistic using the metrics sensitivity, specificity, positive predictive value, and negative predictive value, the discriminating factor of the additive score was determined.

RESULTS

The mean age of the 1021 patients who needed endotracheal intubations was 52.2 years (± 17.5), and 632 (62%) of them were male. In the development dataset, there were 527 (64.9%) physiologically difficult airways, 298 (36.7%) post-intubation hypotension, 124 (12%) cardiac arrest, 347 (42.7%) shock index > 0.9, and 456 [56.2%] instances of pH < 7.3. On the contrary, in the validation dataset, there were 143 (68.4%) physiologically difficult airways, 33 (15.8%) post-intubation hypotension, 41 (19.6%) cardiac arrest, 87 (41.6%) shock index > 0.9, and 121 (57.9%) had pH < 7.3, respectively. There were 12 variables in the difficult airway physiological score (DAPS), and a DAPS of 9 had an area under the curve of 0.857. The accuracy of DAPS was 77%, the sensitivity was 74%, the specificity was 83.3%, and the positive predictive value was 91%.

CONCLUSION

DAPS demonstrated strong discriminating ability for anticipating physiologically challenging airways. The proposed model may be helpful in the clinical setting for screening patients who are at high risk of deterioration.

Modest performance of text mining to extract health outcomes may be almost sufficient for high-quality prognostic model development

BACKGROUND

Across medicine, prognostic models are used to estimate patient risk of certain future health outcomes (e.g., cardiovascular or mortality risk). To develop (or train) prognostic models, historic patient-level training data is needed containing both the predictive factors (i.e., features) and the relevant health outcomes (i.e., labels). Sometimes, when the health outcomes are not recorded in structured data, these are first extracted from textual notes using text mining techniques. Because there exist many studies utilizing text mining to obtain outcome data for prognostic model development, our aim is to study the impact of the text mining quality on downstream prognostic model performance.

METHODS

We conducted a simulation study charting the relationship between text mining quality and prognostic model performance using an illustrative case study about in-hospital mortality prediction in intensive care unit patients. We repeatedly developed and evaluated a prognostic model for in-hospital mortality, using outcome data extracted by multiple text mining models of varying quality.

RESULTS

Interestingly, we found in our case study that a relatively low-quality text mining model (F1 score ≈ 0.50) could already be used to train a prognostic model with quite good discrimination (area under the receiver operating characteristic curve of around 0.80). The calibration of the risks estimated by the prognostic model seemed unreliable across the majority of settings, even when text mining models were of relatively high quality (F1 ≈ 0.80).

DISCUSSION

Developing prognostic models on text-extracted outcomes using imperfect text mining models seems promising. However, it is likely that prognostic models developed using this approach may not produce well-calibrated risk estimates, and require recalibration in (possibly a smaller amount of) manually extracted outcome data.

The risk of fractures after entering long-term care facilities

BACKGROUND

Stratifying residents at increased risk for fractures in long-term care facilities (LTCFs) can potentially improve awareness and facilitate the delivery of targeted interventions to reduce risk. Although several fracture risk assessment tools exist, most are not suitable for individuals entering LTCF. Moreover, existing tools do not examine risk profiles of individuals at key periods in their aged care journey, specifically at entry into LTCFs.

PURPOSE

Our objectives were to identify fracture predictors, develop a fracture risk prognostic model for new LTCF residents and compare its performance to the Fracture Risk Assessment in Long term care (FRAiL) model using the Registry of Senior Australians (ROSA) Historical National Cohort, which contains integrated health and aged care information for individuals receiving long term care services.

METHODS

Individuals aged ≥65 years old who entered 2079 facilities in three Australian states between 01/01/2009 and 31/12/2016 were examined. Fractures (any) within 365 days of LTCF entry were the outcome of interest. Individual, medication, health care, facility and system-related factors were examined as predictors. A fracture prognostic model was developed using elastic nets penalised regression and Fine-Gray models. Model discrimination was examined using area under the receiver operating characteristics curve (AUC) from the 20 % testing dataset. Model performance was compared to an existing risk model (i.e., FRAiL model).

RESULTS

Of the 238,782 individuals studied, 62.3 % (N = 148,838) were women, 49.7 % (N = 118,598) had dementia and the median age was 84 (interquartile range 79-89). Within 365 days of LTCF entry, 7.2 % (N = 17,110) of individuals experienced a fracture. The strongest fracture predictors included: complex health care rating (no vs high care needs, sub-distribution hazard ratio (sHR) = 1.52, 95 % confidence interval (CI) 1.39-1.67), nutrition rating (moderate vs worst, sHR = 1.48, 95%CI 1.38-1.59), prior fractures (sHR ranging from 1.24 to 1.41 depending on fracture site/type), one year history of general practitioner attendances (≥16 attendances vs none, sHR = 1.35, 95%CI 1.18-1.54), use of dopa and dopa derivative antiparkinsonian medications (sHR = 1.28, 95%CI 1.19-1.38), history of osteoporosis (sHR = 1.22, 95%CI 1.16-1.27), dementia (sHR = 1.22, 95%CI 1.17-1.28) and falls (sHR = 1.21, 95%CI 1.17-1.25). The model AUC in the testing cohort was 0.62 (95%CI 0.61-0.63) and performed similar to the FRAiL model (AUC = 0.61, 95%CI 0.60-0.62).

CONCLUSIONS

Critical information captured during transition into LTCF can be effectively leveraged to inform fracture risk profiling. New fracture predictors including complex health care needs, recent emergency department encounters, general practitioner and consultant physician attendances, were identified.

A literature review of quality assessment and applicability to HTA of risk prediction models of coronary heart disease in patients with diabetes

This literature review had two objectives: to identify models for predicting the risk of coronary heart diseases in patients with diabetes (DM); and to assess model quality in terms of risk of bias (RoB) and applicability for the purpose of health technology assessment (HTA). We undertook a targeted review of journal articles published in English, Dutch, Chinese, or Spanish in 5 databases from 1st January 2016 to 18th December 2022, and searched three systematic reviews for the models published after 2012. We used PROBAST (Prediction model Risk Of Bias Assessment Tool) to assess RoB, and used findings from Betts et al. 2019, which summarized recommendations and criticisms of HTA agencies on cardiovascular risk prediction models, to assess model applicability for the purpose of HTA. As a result, 71 % and 67 % models reporting C-index showed good discrimination abilities (C-index >= 0.7). Of the 26 model studies and 30 models identified, only one model study showed low RoB in all domains, and no model was fully applicable for HTA. Since the major cause of high RoB is inappropriate use of analysis method, we advise clinicians to carefully examine the model performance declared by model developers, and to trust a model if all PROBAST domains except analysis show low RoB and at least one validation study conducted in the same setting (e.g. country) is available. Moreover, since general model applicability is not informative for HTA, novel adapted tools may need to be developed.

Predictive nomogram using multimodal ultrasonographic features for axillary lymph node metastasis in early‑stage invasive breast cancer

Axillary lymph node (ALN) status is a key prognostic factor in patients with early-stage invasive breast cancer (IBC). The present study aimed to develop and validate a nomogram based on multimodal ultrasonographic (MMUS) features for early prediction of axillary lymph node metastasis (ALNM). A total of 342 patients with early-stage IBC (240 in the training cohort and 102 in the validation cohort) who underwent preoperative conventional ultrasound (US), strain elastography, shear wave elastography and contrast-enhanced US examination were included between August 2021 and March 2022. Pathological ALN status was used as the reference standard. The clinicopathological factors and MMUS features were analyzed with uni- and multivariate logistic regression to construct a clinicopathological and conventional US model and a MMUS-based nomogram. The MMUS nomogram was validated with respect to discrimination, calibration, reclassification and clinical usefulness. US features of tumor size, echogenicity, stiff rim sign, perfusion defect, radial vessel and US Breast Imaging Reporting and Data System category 5 were independent risk predictors for ALNM. MMUS nomogram based on these factors demonstrated an improved calibration and favorable performance [area under the receiver operator characteristic curve (AUC), 0.927 and 0.922 in the training and validation cohorts, respectively] compared with the clinicopathological model (AUC, 0.681 and 0.670, respectively), US-depicted ALN status (AUC, 0.710 and 0.716, respectively) and the conventional US model (AUC, 0.867 and 0.894, respectively). MMUS nomogram improved the reclassification ability of the conventional US model for ALNM prediction (net reclassification improvement, 0.296 and 0.288 in the training and validation cohorts, respectively; both P<0.001). Taken together, the findings of the present study suggested that the MMUS nomogram may be a promising, non-invasive and reliable approach for predicting ALNM.

The Improved Kidney Risk Score in ANCA-Associated Vasculitis for Clinical Practice and Trials

SIGNIFICANCE STATEMENT

Reliable prediction tools are needed to personalize treatment in ANCA-associated GN. More than 1500 patients were collated in an international longitudinal study to revise the ANCA kidney risk score. The score showed satisfactory performance, mimicking the original study (Harrell's C=0.779). In the development cohort of 959 patients, no additional parameters aiding the tool were detected, but replacing the GFR with creatinine identified an additional cutoff. The parameter interstitial fibrosis and tubular atrophy was modified to allow wider access, risk points were reweighted, and a fourth risk group was created, improving predictive ability (C=0.831). In the validation, the new model performed similarly well with excellent calibration and discrimination ( n =480, C=0.821). The revised score optimizes prognostication for clinical practice and trials.

BACKGROUND

Reliable prediction tools are needed to personalize treatment in ANCA-associated GN. A retrospective international longitudinal cohort was collated to revise the ANCA renal risk score.

METHODS

The primary end point was ESKD with patients censored at last follow-up. Cox proportional hazards were used to reweight risk factors. Kaplan-Meier curves, Harrell's C statistic, receiver operating characteristics, and calibration plots were used to assess model performance.

RESULTS

Of 1591 patients, 1439 were included in the final analyses, 2:1 randomly allocated per center to development and validation cohorts (52% male, median age 64 years). In the development cohort ( n =959), the ANCA renal risk score was validated and calibrated, and parameters were reinvestigated modifying interstitial fibrosis and tubular atrophy allowing semiquantitative reporting. An additional cutoff for kidney function (K) was identified, and serum creatinine replaced GFR (K0: <250 µ mol/L=0, K1: 250-450 µ mol/L=4, K2: >450 µ mol/L=11 points). The risk points for the percentage of normal glomeruli (N) and interstitial fibrosis and tubular atrophy (T) were reweighted (N0: >25%=0, N1: 10%-25%=4, N2: <10%=7, T0: none/mild or <25%=0, T1: ≥ mild-moderate or ≥25%=3 points), and four risk groups created: low (0-4 points), moderate (5-11), high (12-18), and very high (21). Discrimination was C=0.831, and the 3-year kidney survival was 96%, 79%, 54%, and 19%, respectively. The revised score performed similarly well in the validation cohort with excellent calibration and discrimination ( n =480, C=0.821).

CONCLUSIONS

The updated score optimizes clinicopathologic prognostication for clinical practice and trials.

Multivariable machine learning models for clinical prediction of subsequent hip fractures in older people using the Chinese population database

PURPOSE

This study aimed to develop and validate clinical prediction models using machine learning (ML) algorithms for reliable prediction of subsequent hip fractures in older individuals, who had previously sustained a first hip fracture, and facilitate early prevention and diagnosis, therefore effectively managing rapidly rising healthcare costs in China.

METHODS

Data were obtained from Grade A Tertiary hospitals for older patients (age ≥ 60 years) diagnosed with hip fractures in southwest China between 1 January 2009 and 1 April 2020. The database was built by collecting clinical and administrative data from outpatients and inpatients nationwide. Data were randomly split into training (80%) and testing datasets (20%), followed by six ML-based prediction models using 19 variables for hip fracture patients within 2 years of the first fracture.

RESULTS

A total of 40,237 patients with a median age of 66.0 years, who were admitted to acute-care hospitals for hip fractures, were randomly split into a training dataset (32,189 patients) and a testing dataset (8,048 patients). Our results indicated that three of our ML-based models delivered an excellent prediction of subsequent hip fracture outcomes (the area under the receiver operating characteristics curve: 0.92 (0.91-0.92), 0.92 (0·92-0·93), 0.92 (0·92-0·93)), outperforming previous prediction models based on claims and cohort data.

CONCLUSIONS

Our prediction models identify Chinese older people at high risk of subsequent hip fractures with specific baseline clinical and demographic variables such as length of hospital stay. These models might guide future targeted preventative treatments.

Development and validation of a simplified risk prediction model for preterm birth: a prospective cohort study in rural Ethiopia

Preterm birth is one of the most common obstetric complications in low- and middle-income countries, where access to advanced diagnostic tests and imaging is limited. Therefore, we developed and validated a simplified risk prediction tool to predict preterm birth based on easily applicable and routinely collected characteristics of pregnant women in the primary care setting. We used a logistic regression model to develop a model based on the data collected from 481 pregnant women. Model accuracy was evaluated through discrimination (measured by the area under the Receiver Operating Characteristic curve; AUC) and calibration (via calibration graphs and the Hosmer-Lemeshow goodness of fit test). Internal validation was performed using a bootstrapping technique. A simplified risk score was developed, and the cut-off point was determined using the "Youden index" to classify pregnant women into high or low risk for preterm birth. The incidence of preterm birth was 19.5% (95% CI:16.2, 23.3) of pregnancies. The final prediction model incorporated mid-upper arm circumference, gravidity, history of abortion, antenatal care, comorbidity, intimate partner violence, and anemia as predictors of preeclampsia. The AUC of the model was 0.687 (95% CI: 0.62, 0.75). The calibration plot demonstrated a good calibration with a p-value of 0.713 for the Hosmer-Lemeshow goodness of fit test. The model can identify pregnant women at high risk of preterm birth. It is applicable in daily clinical practice and could contribute to the improvement of the health of women and newborns in primary care settings with limited resources. Healthcare providers in rural areas could use this prediction model to improve clinical decision-making and reduce obstetrics complications.

Prognostic model for overall survival of head and neck cancer patients in the palliative phase

BACKGROUND

Patients with head and neck squamous cell carcinoma (HNSCC) enter the palliative phase when cure is no longer possible or when they refuse curative treatment. The mean survival is five months, with a range of days until years. Realistic prognostic counseling enables patients to make well-considered end-of-life choices. However, physicians tend to overestimate survival. The aim of this study was to develop a prognostic model that calculates the overall survival (OS) probability of palliative HNSCC patients.

METHODS

Patients diagnosed with incurable HNSCC or patients who refused curative treatment for HNSCC between January 1st 2006 and June 3rd 2019 were included (n = 659). Three patients were lost to follow-up. Patients were considered to have incurable HNSCC due to tumor factors (e.g. inoperability with no other curative treatment options, distant metastasis) or patient factors (e.g. the presence of severe comorbidity and/or poor performance status).Tumor and patients factors accounted for 574 patients. An additional 82 patients refused curative treatment and were also considered palliative. The effect of 17 candidate predictors was estimated in the univariable cox proportional hazard regression model. Using backwards selection with a cut-off P-value < 0.10 resulted in a final multivariable prediction model. The C-statistic was calculated to determine the discriminative performance of the model. The final model was internally validated using bootstrapping techniques.

RESULTS

A total of 647 patients (98.6%) died during follow-up. Median OS time was 15.0 weeks (95% CI: 13.5;16.6). Of the 17 candidate predictors, seven were included in the final model: the reason for entering the palliative phase, the number of previous HNSCC, cT, cN, cM, weight loss in the 6 months before diagnosis, and the WHO performance status. The internally validated C-statistic was 0.66 indicating moderate discriminative ability. The model showed some optimism, with a shrinkage factor of 0.89.

CONCLUSION

This study enabled the development and internal validation of a prognostic model that predicts the OS probability in HNSCC patients in the palliative phase. This model facilitates personalized prognostic counseling in the palliative phase. External validation and qualitative research are necessary before widespread use in patient counseling and end-of-life care.

Prediction of unplanned cesarean section using measurable maternal and fetal characteristics, Ethiopia, a retrospective cohort study

BACKGROUND

When a pregnant woman experiences unusual circumstances during a vaginal delivery, an unplanned cesarean section may be necessary to save her life. It requires knowledge and quick assessment of the risky situation to decide to perform an unplanned cesarean section, which only occurs in specific obstetric situations. This study aimed to develop and validate a risk prediction model for unplanned cesarean sections among laboring women in Ethiopia.

METHOD

A retrospective follow-up study was conducted. The data were extracted using a structured checklist. Analysis was done using STATA version 14 and R version 4.2.2 software. Logistic regression was fitted to determine predictors of unplanned cesarean sections. Significant variables were then used to develop a risk prediction model. Performance was assessed using Area Under the Receiver Operating Curve (AUROC) and calibration plot. Internal validation was performed using the bootstrap technique. The clinical benefit of the model was assessed using decision curve analysis.

RESULT

A total of 1,000 laboring women participated in this study; 28.5% were delivered by unplanned cesarean section. Parity, amniotic fluid status, gestational age, prolonged labor, the onset of labor, amount of amniotic fluid, previous mode of delivery, and abruption remained in the reduced multivariable logistic regression and were used to develop a prediction risk score with a total score of 9. The AUROC was 0.82. The optimal cut-off point for risk categorization as low and high was 6, with a sensitivity (85.2%), specificity (90.1%), and accuracy (73.9%). After internal validation, the optimism coefficient was 0.0089. The model was found to have clinical benefits.

CONCLUSION

To objectively measure the risk of an unplanned Caesarean section, a risk score model based on measurable maternal and fetal attributes has been developed. The score is simple, easy to use, and repeatable in clinical practice.

Development of a Prediction Model for Surgery or Early Mortality at the Time of Initial Assessment for Necrotizing Enterocolitis

OBJECTIVE

 No available scale, at the time of initial evaluation for necrotizing enterocolitis (NEC), accurately predicts, that is, with an area under the curve (AUC) ≥0.9, which preterm infants will undergo surgery for NEC stage III or die within a week.

STUDY DESIGN

 This is a retrospective cohort study (n = 261) of preterm infants with <33 weeks' gestation or <1,500 g birthweight with either suspected or with definite NEC born at Parkland Hospital between 2009 and 2021. A prediction model using the new HASOFA SCORE (H: yperglycemia, H: yperkalemia, use of inotropes for H: ypotension during the prior week, A: cidemia, Neonatal S: equential O: rgan F: ailure A: ssessment [nSOFA: ] score) was compared with a similar model using the nSOFA score.

RESULTS

 Among 261 infants, 112 infants had NEC stage I, 68 with NEC stage II, and 81 with NEC stage III based on modified Bell's classification. The primary outcome, surgery for NEC stage III or death within a week, occurred in 81 infants (surgery in 66 infants and death in 38 infants). All infants with pneumoperitoneum or abdominal compartment syndrome either died or had surgery. The HASOFA and the nSOFA scores were evaluated in 254 and 253 infants, respectively, at the time of the initial workup for NEC. Both models were internally validated. The HASOFA model was a better predictor of surgery for NEC stage III or death within a week than the nSOFA model, with greater AUC 0.909 versus 0.825, respectively, p < 0.001. Combining HASOFA at initial assessment with concurrent or later presence of abdominal wall erythema or portal gas improved the prediction surgery for NEC stage III or death with AUC 0.942 or 0.956, respectively.

CONCLUSION

 Using this new internally validated prediction model, surgery for NEC stage III or death within a week can be accurately predicted at the time of initial assessment for NEC.

KEY POINTS

· No available scale, at initial evaluation, accurately predicts which preterm infants will undergo surgery for NEC stage III or die within a week.. · In this retrospective cohort study of 261 preterm infants with either suspected or definite NEC we developed a new prediction model (HASOFA score).. · The HASOFA-model had high discrimination (AUC 0.909) and excellent calibration and was internally validated..

Personalized decision-making for aneurysm treatment of aneurysmal subarachnoid hemorrhage: development and validation of a clinical prediction tool

BACKGROUND

In patients with aneurysmal subarachnoid hemorrhage suitable for endovascular coiling and neurosurgical clip-reconstruction, the aneurysm treatment decision-making process could be improved by considering heterogeneity of treatment effect and durability of treatment. We aimed to develop and validate a tool to predict individualized treatment benefit of endovascular coiling compared to neurosurgical clip-reconstruction.

METHODS

We used randomized data (International Subarachnoid Aneurysm Trial, n = 2143) to develop models to predict 2-month functional outcome and to predict time-to-rebleed-or-retreatment. We modeled for heterogeneity of treatment effect by adding interaction terms of treatment with prespecified predictors and with baseline risk of the outcome. We predicted outcome with both treatments and calculated absolute treatment benefit. We described the patient characteristics of patients with ≥ 5% point difference in the predicted probability of favorable functional outcome (modified Rankin Score 0-2) and of no rebleed or retreatment within 10 years. Model performance was expressed with the c-statistic and calibration plots. We performed bootstrapping and leave-one-cluster-out cross-validation and pooled cluster-specific c-statistics with random effects meta-analysis.

RESULTS

The pooled c-statistics were 0.72 (95% CI: 0.69-0.75) for the prediction of 2-month favorable functional outcome and 0.67 (95% CI: 0.63-0.71) for prediction of no rebleed or retreatment within 10 years. We found no significant interaction between predictors and treatment. The average predicted benefit in favorable functional outcome was 6% (95% CI: 3-10%) in favor of coiling, but 11% (95% CI: 9-13%) for no rebleed or retreatment in favor of clip-reconstruction. 134 patients (6%), young and in favorable clinical condition, had negligible functional outcome benefit of coiling but had a ≥ 5% point benefit of clip-reconstruction in terms of durability of treatment.

CONCLUSIONS

We show that young patients in favorable clinical condition and without extensive vasospasm have a negligible benefit in functional outcome of endovascular coiling - compared to neurosurgical clip-reconstruction - while at the same time having a substantially lower probability of retreatment or rebleeding from neurosurgical clip-reconstruction - compared to endovascular coiling. The SHARP prediction tool ( https://sharpmodels.shinyapps.io/sharpmodels/ ) could support and incentivize a multidisciplinary discussion about aneurysm treatment decision-making by providing individualized treatment benefit estimates.

Development of a risk score to predict occurrence of deep sternal dehiscence requiring operative debridement

OBJECTIVE

Severe deep sternal wound (DSW) complications after cardiac surgery are a source of cost, morbidity, and mortality. Our objective was to develop and validate a clinical risk score for predicting risk of DSW requiring operative bone debridement, the most severe form of sternal dehiscence.

METHODS

A retrospective review was conducted of patients who underwent open cardiac surgery at a single institution between October 2007 and March 2019. Primary outcome was DSW requiring sternal bone debridement. Potential risk factors were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and significant covariates were included in a logistic regression prediction model. Interval validation was performed using 10-fold cross-validation. A novel sternal wound dehiscence risk score was derived from the relative parameterization estimates.

RESULTS

One hundred thirty-four of 8403 patients (1.6%) were identified as having a DSW. Female sex (odds ratio [OR], 2.75; 95% CI, 2.58-2.93), body mass index (OR, 1.0946; 95% CI, 1.09-1.09), percent glycated hemoglobin (OR, 1.31; 95% CI, 1.28-1.33), peripheral vascular disease (OR, 2.38; 95% CI, 2.2005-2.5752), smoking (OR, 1.66; 95% CI, 1.53-1.79) and elevated creatinine level (OR, 1.20; 95% CI, 1.18-1.22) were independent predictors of DSW. Patients were categorized as minimal risk (0%-1%), low risk (2%-3%), intermediate risk (4%-7%), and high risk (9%-64.0%) on the basis of risk score.

CONCLUSIONS

This risk stratification model for DSW requiring operative debridement might provide individualized estimates of risk, and guide counseling and potential risk mitigation strategies.

Comparing the accuracy of prediction models to detect clinically relevant post-hepatectomy liver failure early after major hepatectomy

BACKGROUND

Arterial lactate measurements were recently suggested as an early predictor of clinically relevant post-hepatectomy liver failure (PHLF). This needed to be evaluated in the subgroup of major hepatectomies only.

METHOD

This observational cohort study included consecutive elective major hepatectomies at Karolinska University Hospital from 2010 to 2018. Clinical risk factors for PHLF, perioperative arterial lactate measurements and routine lab values were included in uni- and multivariable regression analysis. Receiver operating characteristics and risk cut-offs were calculated.

RESULTS

In total, 649 patients constituted the study cohort, of which 92 developed PHLF grade B/C according to the International Study Group of Liver Surgery (ISGLS). Lactate reached significantly higher intra- and postoperative levels in PHLF grades B and C compared to grade A or no liver failure (all P < 0.002). Lactate on postoperative day (POD) 1 was superior to earlier measurement time points in predicting PHLF B/C (AUC 0.75), but was outperformed by both clinical risk factors (AUC 0.81, P = 0.031) and bilirubin POD1 (AUC 0.83, P = 0.013). A multivariable logistic regression model including clinical risk factors and bilirubin POD1 had the highest AUC of 0.87 (P = 0.006), with 56.6% sensitivity and 94.7% specificity for PHLF grade B/C (cut-off ≥0.32). The model identified 46.7% of patients with 90-day mortality and had an equally good discriminatory potential for mortality as the established ISGLS criteria for PHLF grade B/C but could be applied already on POD1.

CONCLUSION

The potential of lactate to predict PHLF following major hepatectomy was inferior to a prediction model consisting of clinical risk factors and bilirubin on first post-operative day.

Clinical characteristics and prognosis analysis of postoperative patients with stage I-III colon cancer based on SEER database

PURPOSE

To identify the relevant factors affecting the prognosis and survival time of colon cancer and construct a survival prediction model.

METHODS

Data on postoperative stage I-III colon cancer patients were obtained from the Surveillance, Epidemiology, and End Results database. We used R project to analyze the data. Univariate and multivariate Cox regression analyses were performed for independent factors correlated with overall survival from colon cancer. The C-index was used to screen the factors that had the greatest influence in overall survival after surgery in colon cancer patients. Receiver operating characteristic (ROC) curve was made according to the Risk score and calculated to validate the predictive accuracy of the model. In addition, we used decision curve analysis (DCA) to evaluate the clinical benefits and utility of the nomogram. We created a model survival curve to determine the difference in prognosis between patients in the low-risk group and those in the high-risk group.

RESULTS

Univariate and multifactor COX analyses showed that the race, Grade, tumor size, N-stage and T-stage were independent risk factors affecting survival time of patients. The analysis of ROC and DCA showed the nomogram prediction model constructed based on the above indicators has good predictive effects.

CONCLUSION

Overall, the nomogram constructed in this study has good predictive effects. It can provide a reference for future clinicians to evaluate the prognosis of colon cancer patients.

Development of a multivariable prediction model for prolonged air leak after lung resection

OBJECTIVES

Prolonged air leak (PAL) is a common complication of lung resection. Research on predictors of PAL using a digital drainage system (DDS) remains insufficient. In this study, we investigated the predictive factors of PAL to establish a novel early postoperative prediction model for PAL.

METHODS

A retrospective cohort study and validation study were conducted. We examined patients who underwent lung resection with DDS at our institute. The relationship between the clinical factors and measurements of the DDS, including the difference between the set and measured intrapleural pressure (named: additional negative pressure [ANP]) at postoperative hour (POH) 3, with PAL was analyzed.

RESULTS

A total of 494 patients were enrolled, 29 of whom had PAL. Percent forced expiratory volume in 1 s <60%, ANP <1 cmH2O, air leak flow >20 mL/min and pleural adhesion findings at surgery were independent predictors of PAL according to a multivariable analysis. The PAL rate was clearly stratified according to our novel risk scoring system, which simply notes the presence of the above four factors, that is, the rate increases when the score increases. The area under the curve (AUC) of the receiver operating characteristic (ROC) analysis for this scoring system was 0.818. Analysis of the validation cohort (n = 133) revealed that this scoring system showed a sufficient ability to predict PAL.

CONCLUSIONS

ANP at POH 3 is an independent predictor of PAL. Thus, the risk-scoring system proposed in this study is useful for predicting PAL in the early postoperative period.

End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study

BACKGROUND

Precise prognosis prediction in patients with colorectal cancer (ie, forecasting survival) is pivotal for individualised treatment and care. Histopathological tissue slides of colorectal cancer specimens contain rich prognostically relevant information. However, existing studies do not have multicentre external validation with real-world sample processing protocols, and algorithms are not yet widely used in clinical routine.

METHODS

In this retrospective, multicentre study, we collected tissue samples from four groups of patients with resected colorectal cancer from Australia, Germany, and the USA. We developed and externally validated a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with resected colorectal cancer. We used the model-predicted risk scores to stratify patients into different risk groups and compared survival outcomes between these groups. Additionally, we evaluated the prognostic value of these risk groups after adjusting for established prognostic variables.

FINDINGS

We trained and validated our model on a total of 4428 patients. We found that patients could be divided into high-risk and low-risk groups on the basis of the deep learning-based risk score. On the internal test set, the group with a high-risk score had a worse prognosis than the group with a low-risk score, as reflected by a hazard ratio (HR) of 4·50 (95% CI 3·33-6·09) for overall survival and 8·35 (5·06-13·78) for disease-specific survival (DSS). We found consistent performance across three large external test sets. In a test set of 1395 patients, the high-risk group had a lower DSS than the low-risk group, with an HR of 3·08 (2·44-3·89). In two additional test sets, the HRs for DSS were 2·23 (1·23-4·04) and 3·07 (1·78-5·3). We showed that the prognostic value of the deep learning-based risk score is independent of established clinical risk factors.

INTERPRETATION

Our findings indicate that attention-based self-supervised deep learning can robustly offer a prognosis on clinical outcomes in patients with colorectal cancer, generalising across different populations and serving as a potentially new prognostic tool in clinical decision making for colorectal cancer management. We release all source codes and trained models under an open-source licence, allowing other researchers to reuse and build upon our work.

FUNDING

The German Federal Ministry of Health, the Max-Eder-Programme of German Cancer Aid, the German Federal Ministry of Education and Research, the German Academic Exchange Service, and the EU.

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.

A model to predict level of adherence to prehabilitation in older adults with frailty having cancer surgery

PURPOSE

Preoperative exercise could improve postoperative outcomes for people with frailty; however, little is known about how to predict older people's adherence to exercise before surgery (i.e., prehabilitation) programs. Our objective was to derive and validate a model to predict prehabilitation adherence in older adults living with frailty before cancer surgery.

METHODS

This was a nested prospective cohort study of older adults with frailty having cancer surgery who participated in a randomized controlled trial of home-based prehabilitation compared with standard perioperative care. We constructed a multivariable ordinary least squares linear regression model to predict adherence. Covariates were selected a priori based on clinical expertise and systematic review. Optimism was estimated through internal validation using bootstrap resampling.

RESULTS

The derivation cohort consisted of 95 participants in the intervention arm of the trial. Percent adherence ranged from 0% to 100%, with a mean (standard deviation) of 61 (34)%. Previous physical activity and age were the only predictors significant at the 5% level.

CONCLUSION

A prespecified multivariable model may help to explain a modest degree of variation in prehabilitation adherence in older people with frailty. While this model is an important step toward personalizing prehabilitation support, this study was limited by a small sample size and future research is needed to better understand personalized prediction of prehabilitation adherence in older people with frailty.

A Predictive Model for Kidney Failure After Nephrectomy for Localized Kidney Cancer: The Kidney Cancer Risk Equation

RATIONALE & OBJECTIVE

Nephrectomy is the mainstay of treatment for individuals with localized kidney cancer. However, surgery can potentially result in the loss of kidney function or in kidney failure requiring dialysis/kidney transplantation. There are currently no clinical tools available to preoperatively identify which patients are at risk of kidney failure over the long term. Our study developed and validated a prediction equation for kidney failure after nephrectomy for localized kidney cancer.

STUDY DESIGN

Population-level cohort study.

SETTING & PARTICIPANTS

Adults (n=1,026) from Manitoba, Canada, with non-metastatic kidney cancer diagnosed between January 1, 2004, and December 31, 2016, who were treated with either a partial or radical nephrectomy and had at least 1 estimated glomerular filtration rate (eGFR) measurement before and after nephrectomy. A validation cohort included individuals in Ontario (n=12,043) with a diagnosis of localized kidney cancer between October 1, 2008, and September 30, 2018, who received a partial or radical nephrectomy and had at least 1 eGFR measurement before and after surgery.

NEW PREDICTORS & ESTABLISHED PREDICTORS

Age, sex, eGFR, urinary albumin-creatinine ratio, history of diabetes mellitus, and nephrectomy type (partial/radical).

OUTCOME

The primary outcome was a composite of dialysis, transplantation, or an eGFR<15mL/min/1.73m2 during the follow-up period.

ANALYTICAL APPROACH

Cox proportional hazards regression models evaluated for accuracy using area under the receiver operating characteristic curve (AUC), Brier scores, calibration plots, and continuous net reclassification improvement. We also implemented decision curve analysis. Models developed in the Manitoba cohort were validated in the Ontario cohort.

RESULTS

In the development cohort, 10.3% reached kidney failure after nephrectomy. The final model resulted in a 5-year area under the curve of 0.85 (95% CI, 0.78-0.92) in the development cohort and 0.86 (95% CI, 0.84-0.88) in the validation cohort.

LIMITATIONS

Further external validation needed in diverse cohorts.

CONCLUSIONS

Our externally validated model can be easily applied in clinical practice to inform preoperative discussions about kidney failure risk in patients facing surgical options for localized kidney cancer.

PLAIN-LANGUAGE SUMMARY

Patients with localized kidney cancer often experience a lot of worry about whether their kidney function will remain stable or will decline if they choose to undergo surgery for treatment. To help patients make an informed treatment decision, we developed a simple equation that incorporates 6 easily accessible pieces of patient information to predict the risk of reaching kidney failure 5 years after kidney cancer surgery. We expect that this tool has the potential to inform patient-centered discussions tailored around individualized risk, helping ensure that patients receive the most appropriate risk-based care.

Knowledge and data-driven prediction of organ failure in critical care patients

Purpose

The early detection of organ failure mitigates the risk of post-intensive care syndrome and long-term functional impairment. The aim of this study is to predict organ failure in real-time for critical care patients based on a data-driven and knowledge-driven machine learning method (DKM) and provide explanations for the prediction by incorporating a medical knowledge graph.

Methods

The cohort of this study was a subset of the 4,386 adult Intensive Care Unit (ICU) patients from the MIMIC-III dataset collected between 2001 and 2012, and the primary outcome was the Delta Sequential Organ Failure Assessment (SOFA) score. A real-time Delta SOFA score prediction model was developed with two key components: an improved deep learning temporal convolutional network (S-TCN) and a graph-embedding feature extraction method based on a medical knowledge graph. Entities and relations related to organ failure were extracted from the Unified Medical Language System to build the medical knowledge graph, and patient data were mapped onto the graph to extract the embeddings. We measured the performance of our DKM approach with cross-validation to avoid the formation of biased assessments.

Results

An area under the receiver operating characteristic curve (AUC) of 0.973, a precision of 0.923, a NPV of 0.989, and an F1 score of 0.927 were achieved using the DKM approach, which significantly outperformed the baseline methods. Additionally, the performance remained stable following external validation on the eICU dataset, which consists of 2,816 admissions (AUC = 0.981, precision = 0.860, NPV = 0.984). Visualization of feature importance for the Delta SOFA score and their relationships on the basic clinical medical (BCM) knowledge graph provided a model explanation.

Conclusion

The use of an improved TCN model and a medical knowledge graph led to substantial improvement in prediction accuracy, providing generalizability and an independent explanation for organ failure prediction in critical care patients. These findings show the potential of incorporating prior domain knowledge into machine learning models to inform care and service planning.

Supplementary Information

The online version of this article contains supplementary material available 10.1007/s13755-023-00210-5.

A nomogram for the prediction of survival for colorectal signet ring cell carcinoma after surgery: A population-based study

The aim was to construct and verify a nomogram-based assessment of cancer-specific survival (CSS) in patients with colorectal signet ring cell carcinoma after surgery. Patients were collected from Surveillance, Epidemiology, and End Results program between 2004 and 2015. Independent prognostic indicators were determined in the training cohort by Cox regression model. We identified 2217 eligible patients, who were further categorized into the training set (n = 1693) as well as the validation set (n = 524). Multivariate analysis revealed that age at diagnosis, gender, grade, tumor size, T stage, N stage, and M stage were independent predictive indicators. Then, the above 7 predictive factors were incorporated into a nomogram model to assess CSS, which showed good calibration and discrimination capacities in both sets. Both internal and external calibration plot diagrams revealed that the actual results were consistent with the predicted outcomes. The time-independent area under the curves for 3-year and 5-year CSS in the nomogram were larger than American Joint Committee on Cancer and Surveillance, Epidemiology, and End Results summary stage system. Moreover, decision curve analysis indicated the clinical utility of the nomogram. The nomogram demonstrated favorable predictive accuracy of survival in colorectal signet ring cell carcinoma patients after surgery, which should be further confirmed before clinical implementation.

Limited clinical validity of univariate resting-state EEG markers for classifying seizure disorders

Differentiating between epilepsy and psychogenic non-epileptic seizures presents a considerable challenge in clinical practice, resulting in frequent misdiagnosis, unnecessary treatment and long diagnostic delays. Quantitative markers extracted from resting-state EEG may reveal subtle neurophysiological differences that are diagnostically relevant. Two observational, retrospective diagnostic accuracy studies were performed to test the clinical validity of univariate resting-state EEG markers for the differential diagnosis of epilepsy and psychogenic non-epileptic seizures. Clinical EEG data were collected for 179 quasi-consecutive patients (age > 18) with a suspected diagnosis of epilepsy or psychogenic non-epileptic seizures who were medication-naïve at the time of EEG; 148 age- and gender-matched patients subsequently received a diagnosis from specialist clinicians and were included in the analyses. Study 1 is a hypothesis-driven study testing the ability of theta power and peak alpha frequency to classify people with epilepsy and people with psychogenic non-epileptic seizures, with an advanced machine learning pipeline. The next study (Study 2) is data-driven; a high number of quantitative EEG features are extracted and a similar machine learning approach as Study 1 assesses whether previously unexplored univariate EEG measures show promise as diagnostic markers. The results of Study 1 suggest that EEG markers that were previously identified as promising diagnostic indicators (i.e. theta power and peak alpha frequency) have limited clinical validity for the classification of epilepsy and psychogenic non-epileptic seizures (mean accuracy: 48%). The results of Study 2 indicate that identifying univariate markers that show good correlation with a categorical diagnostic label is challenging (mean accuracy: 45-60%). This is due to a considerable overlap in neurophysiological features between the diagnostic classes considered in this study, and to the presence of more dominant EEG dynamics such as alterations due to temporal proximity to epileptiform discharges. Markers that were identified in the context of previous epilepsy research using visually normal resting-state EEG were found to have limited clinical validity for the classification task of distinguishing between people with epilepsy and people with psychogenic non-epileptic seizures. A search for alternative diagnostic markers uncovered the challenges involved and generated recommendations for further research.

Clinical Prediction Models for Hospital-Induced Delirium Using Structured and Unstructured Electronic Health Record Data: Protocol for a Development and Validation Study

BACKGROUND

Hospital-induced delirium is one of the most common and costly iatrogenic conditions, and its incidence is predicted to increase as the population of the United States ages. An academic and clinical interdisciplinary systems approach is needed to reduce the frequency and impact of hospital-induced delirium.

OBJECTIVE

The long-term goal of our research is to enhance the safety of hospitalized older adults by reducing iatrogenic conditions through an effective learning health system. In this study, we will develop models for predicting hospital-induced delirium. In order to accomplish this objective, we will create a computable phenotype for our outcome (hospital-induced delirium), design an expert-based traditional logistic regression model, leverage machine learning techniques to generate a model using structured data, and use machine learning and natural language processing to produce an integrated model with components from both structured data and text data.

METHODS

This study will explore text-based data, such as nursing notes, to improve the predictive capability of prognostic models for hospital-induced delirium. By using supervised and unsupervised text mining in addition to structured data, we will examine multiple types of information in electronic health record data to predict medical-surgical patient risk of developing delirium. Development and validation will be compliant to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.

RESULTS

Work on this project will take place through March 2024. For this study, we will use data from approximately 332,230 encounters that occurred between January 2012 to May 2021. Findings from this project will be disseminated at scientific conferences and in peer-reviewed journals.

CONCLUSIONS

Success in this study will yield a durable, high-performing research-data infrastructure that will process, extract, and analyze clinical text data in near real time. This model has the potential to be integrated into the electronic health record and provide point-of-care decision support to prevent harm and improve quality of care.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/48521.