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1. Predicting 5‐year dementia conversion in veterans with mild cognitive impairment

   https://doi.org/10.1002/dad2.12572  

   Irwin, Chase ; Tjandra, Donna ; Hu, Chengcheng ; Aggarwal, Vinod ; Lienau, Amanda ; Giordani, Bruno ; Wiens, Jenna ; Migrino, Raymond Q. ( March 2024 , Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring)

   Identifying mild cognitive impairment (MCI) patients at risk for dementia could facilitate early interventions. Using electronic health records (EHRs), we developed a model to predict MCI to all‐cause dementia (ACD) conversion at 5 years.

   Cox proportional hazards model was used to identify predictors of ACD conversion from EHR data in veterans with MCI. Model performance (area under the receiver operating characteristic curve [AUC] and Brier score) was evaluated on a held‐out data subset.

   Of 59,782 MCI patients, 15,420 (25.8%) converted to ACD. The model had good discriminative performance (AUC 0.73 [95% confidence interval (CI) 0.72–0.74]), and calibration (Brier score 0.18 [95% CI 0.17–0.18]). Age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors, while body mass index, alcohol abuse, and sleep apnea were protective factors.

   EHR‐based prediction model had good performance in identifying 5‐year MCI to ACD conversion and has potential to assist triaging of at‐risk patients.

   Of 59,782 veterans with mild cognitive impairment (MCI), 15,420 (25.8%) converted to all‐cause dementia within 5 years.

   Electronic health record prediction models demonstrated good performance (area under the receiver operating characteristic curve 0.73; Brier 0.18).

   Age and vascular‐related morbidities were predictors of dementia conversion.

   Synthetic data was comparable to real data in modeling MCI to dementia conversion.

   An electronic health record–based model using demographic and co‐morbidity data had good performance in identifying veterans who convert from mild cognitive impairment (MCI) to all‐cause dementia (ACD) within 5 years.

   Increased age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors for 5‐year conversion from MCI to ACD.

   High body mass index, alcohol abuse, and sleep apnea were protective factors for 5‐year conversion from MCI to ACD.

   Models using synthetic data, analogs of real patient data that retain the distribution, density, and covariance between variables of real patient data but are not attributable to any specific patient, performed just as well as models using real patient data. This could have significant implications in facilitating widely distributed computing of health‐care data with minimized patient privacy concern that could accelerate scientific discoveries.

    

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2. Continuous Intraoperative Data Analysis Using Machine Learning Reveals Multiple Parameters to Predict Post-CABG Renal Failure

   Potosnak, W. ( January 2021 , The Society of Thoracic Surgeons Annual Meeting)

   The purpose of this study is to utilize machine learning techniques to identify intraoperative parameters that contribute significantly to the development of postoperative renal failure following CABG and predict postoperative renal failure based on these parameters. Continuous intraoperative data were gathered retrospectively from the anaesthesia record and included hemodynamic information such as heart rate, arterial blood pressure, central venous pressure, pulmonary artery pressure, as well as additional information such as ventilator settings, temperature, and medication or fluid administration. Multiple machine learning algorithms were tested with this dataset using 10 fold cross validation with stratified folds and their classification performance was measured using area under the receiver operating characteristic curves (ROC AUC). Continuous intraoperative data gathered from patients undergoing CABG revealed potential targets for early, intraoperative intervention to prevent the development of postoperative renal failure. 

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3. Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication

   https://doi.org/10.1038/s41398-021-01632-z  

   Joyce, Jeremiah B. ; Grant, Caroline W. ; Liu, Duan ; MahmoudianDehkordi, Siamak ; Kaddurah-Daouk, Rima ; Skime, Michelle ; Biernacka, Joanna ; Frye, Mark A. ; Mayes, Taryn ; Carmody, Thomas ; et al ( October 2021 , Translational Psychiatry)

   Combination antidepressant pharmacotherapies are frequently used to treat major depressive disorder (MDD). However, there is no evidence that machine learning approaches combining multi-omics measures (e.g., genomics and plasma metabolomics) can achieve clinically meaningful predictions of outcomes to combination pharmacotherapy. This study examined data from 264 MDD outpatients treated with citalopram or escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) and 111 MDD outpatients treated with combination pharmacotherapies in the Combined Medications to Enhance Outcomes of Antidepressant Therapy (CO-MED) study to predict response to combination antidepressant therapies. To assess whether metabolomics with functionally validated single-nucleotide polymorphisms (SNPs) improves predictability over metabolomics alone, models were trained/tested with and without SNPs. Models trained with PGRN-AMPS’ and CO-MED’s escitalopram/citalopram patients predicted response in CO-MED’s combination pharmacotherapy patients with accuracies of 76.6% (p < 0.01; AUC: 0.85) without and 77.5% (p < 0.01; AUC: 0.86) with SNPs. Then, models trained solely with PGRN-AMPS’ escitalopram/citalopram patients predicted response in CO-MED’s combination pharmacotherapy patients with accuracies of 75.3% (p < 0.05; AUC: 0.84) without and 77.5% (p < 0.01; AUC: 0.86) with SNPs, demonstrating cross-trial replication of predictions. Plasma hydroxylated sphingomyelins were prominent predictors of treatment outcomes. To explore the relationship between SNPs and hydroxylated sphingomyelins, we conducted multi-omics integration network analysis. Sphingomyelins clustered with SNPs and metabolites related to monoamine neurotransmission, suggesting a potential functional relationship. These results suggest that integrating specific metabolites and SNPs achieves accurate predictions of treatment response across classes of antidepressants. Finally, these results motivate functional investigation into how sphingomyelins might influence MDD pathophysiology, antidepressant response, or both.

    

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4. Polarization-sensitive optical coherence tomography for renal tumor detection in ex vivo human kidneys

   https://doi.org/10.1016/j.optlaseng.2023.107900  

   Yan, Feng ; Wang, Chen ; Yan, Yuyang ; Zhang, Qinghao ; Yu, Zhongxin ; Patel, Sanjay G. ; Fung, Kar-Ming ; Tang, Qinggong ( February 2024 , Optics and Lasers in Engineering)

   Kidney cancer is a kind of high mortality cancer because of the difficulty in early diagnosis and the high metastatic dissemination in treatments. The surgical resection of tumors is the most effective treatment for renal cancer patients. However, precise assessment of tumor margins is a challenge during surgical resection. The objective of this study is to demonstrate an optical imaging tool in precisely distinguishing kidney tumor borders and identifying tumor zones from normal tissues to assist surgeons in accurately resecting tumors from kidneys during the surgery. 30 samples from six human kidneys were imaged using polarization-sensitive optical coherence tomography (PS-OCT). Cross-sectional, enface, and spatial information of kidney samples were obtained for microenvironment reconstruction. Polarization parameters (phase retardation, optic axis direction, and degree of polarization uniformity (DOPU) and Stokes parameters (Q, U, and V) were utilized for multiparameter analysis. To verify the detection accuracy of PS-OCT, H&E histology staining and dice-coefficient were utilized to quantify the performance of PS-OCT in identifying tumor borders and regions. In this study, tumor borders were clearly identified by PS-OCT imaging, which outperformed the conventional intensity-based OCT. With H&E histological staining as golden standard, PS-OCT precisely identified the tumor regions and tissue distributions at different locations and different depths based on polarization and Stokes parameters. Compared to the traditional attenuation coefficient quantification method, PS-OCT demonstrated enhanced contrast of tissue characteristics between normal and cancerous tissues due to the birefringence effects. Our results demonstrated that PS-OCT was promising to provide imaging guidance for the surgical resection of kidney tumors and had the potential to be used for other human kidney surgeries in clinics such as renal biopsy. 

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5. A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients

   https://doi.org/10.1038/s41746-021-00467-8  

   Yanamala, Naveena ; Krishna, Nanda H. ; Hathaway, Quincy A. ; Radhakrishnan, Aditya ; Sunkara, Srinidhi ; Patel, Heenaben ; Farjo, Peter ; Patel, Brijesh ; Sengupta, Partho P. ( June 2021 , npj Digital Medicine)

   Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have a different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs COVID-19-positive model had an AUC of 98.8%, and 92.8% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available athttps://github.com/ynaveena/COVID-19-vs-Influenzaand may have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.

    

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