Statistical learning theory for high dimensional prediction:Application to criterion-keyed scale development

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in “big data” problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how three common SLT algorithms–Supervised Principal Components, Regularization, and Boosting—can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach—or perhaps because of them–SLT methods may hold value as a statistically rigorous approach to exploratory regression

Development and characterization of a Yucatan miniature biomedical pig permanent middle cerebral artery occlusion stroke model

BACKGROUND: Efforts to develop stroke treatments have met with limited success despite an intense need to produce novel treatments. The failed translation of many of these therapies in clinical trials has lead to a close examination of the therapeutic development process. One of the major factors believed to be limiting effective screening of these treatments is the absence of an animal model more predictive of human responses to treatments. The pig may potentially fill this gap with a gyrencephalic brain that is larger in size with a more similar gray-white matter composition to humans than traditional stroke animal models. In this study we develop and characterize a novel pig middle cerebral artery occlusion (MCAO) ischemic stroke model. METHODS: Eleven male pigs underwent MCAO surgery with the first 4 landrace pigs utilized to optimize stroke procedure and 7 additional Yucatan stroked pigs studied over a 90 day period. MRI analysis was done at 24 hrs and 90 days and included T2w, T2w FLAIR, T1w FLAIR and DWI sequences and associated ADC maps. Pigs were sacrificed at 90 days and underwent gross and microscopic histological evaluation. Significance in quantitative changes was determined by two-way analysis of variance and post-hoc Tukey’s Pair-Wise comparisons. RESULTS: MRI analysis of animals that underwent MCAO surgery at 24 hrs had hyperintense regions in T2w and DWI images with corresponding ADC maps having hypointense regions indicating cytotoxic edema consistent with an ischemic stroke. At 90 days, region of interest analysis of T1 FLAIR and ADC maps had an average lesion size of 59.17 cc, a loss of 8% brain matter. Histological examination of pig brains showed atrophy and loss of tissue, consistent with MRI, as well as glial scar formation and macrophage infiltration. CONCLUSIONS: The MCAO procedure led to significant and consistent strokes with high survivability. These results suggest that the pig model is potentially a robust system for the study of stroke pathophysiology and potential diagnostics and therapeutics

Quality of Life of Caregivers of Older Patients with Advanced Cancer

OBJECTIVES: To evaluate the relationships between aging-related domains captured by geriatric assessment (GA) for older patients with advanced cancer and caregivers’ emotional health and quality of life (QOL). DESIGN: In this cross sectional study of baseline data from a nationwide investigation of older patients and their caregivers, patients completed a GA that included validated tests to evaluate eight domains of health (eg, function, cognition). SETTING: Thirty-one community oncology practices throughout the United States. PARTICIPANTS: Enrolled patients were aged 70 and older, had one or more GA domain impaired, and had an incurable solid tumor malignancy or lymphoma. Each could choose one caregiver to enroll. MEASUREMENTS: Caregivers completed the Generalized Anxiety Disorder-7, Distress Thermometer, Patient Health Questionnaire-2 (depression), and Short Form Health Survey-12 (SF-12 for QOL). Separate multivariate linear or logistic regression models were used to examine the association of the number and type of patient GA impairments with caregiver outcomes, controlling for patient and caregiver covariates. RESULTS: A total of 541 patients were enrolled, 414 with a caregiver. Almost half (43.5%) of the caregivers screened positive for distress, 24.4% for anxiety, and 18.9% for depression. Higher numbers of patient GA domain impairments were associated with caregiver depression (adjusted odds ratio [aOR] = 1.29; P <.001], caregiver physical health on SF-12 (regression coefficient [β] = −1.24; P <.001), and overall caregiver QOL (β = −1.14; P <.01). Impaired patient function was associated with lower caregiver QOL (β = −4.11; P <.001). Impaired patient nutrition was associated with caregiver depression (aOR = 2.08; P <.01). Lower caregiver age, caregiver comorbidity, and patient distress were also associated with worse caregiver outcomes. CONCLUSION: Patient GA impairments were associated with poorer emotional health and lower QOL of caregivers

Association of Prognostic Understanding with Health Care Use among Older Adults with Advanced Cancer: A Secondary Analysis of a Cluster Randomized Clinical Trial

Importance: A poor prognostic understanding regarding curability is associated with lower odds of hospice use among patients with cancer. However, the association between poor prognostic understanding or prognostic discordance and health care use among older adults with advanced incurable cancers is not well characterized. Objective: To evaluate the association of poor prognostic understanding and patient-oncologist prognostic discordance with hospitalization and hospice use among older adults with advanced cancers. Design, Setting, and Participants: This was a post hoc secondary analysis of a cluster randomized clinical trial that recruited patients from October 29, 2014, to April 28, 2017. Data were collected from community oncology practices affiliated with the University of Rochester Cancer Center National Cancer Institute Community Oncology Research Program. The parent trial enrolled 541 patients who were aged 70 years or older and were receiving or considering any line of cancer treatment for incurable solid tumors or lymphomas; the patients' oncologists and caregivers (if available) were also enrolled. Patients were followed up for at least 1 year. Data were analyzed from January 3 to 16, 2021. Main Outcomes and Measures: At enrollment, patients and oncologists were asked about their beliefs regarding cancer curability (100%, >50%, 50%, 5 years; answers of >5 years reflected poor prognostic understanding). Any difference between oncologist and patient in response options was considered discordant. Outcomes were any hospitalization and hospice use at 6 months captured by the clinical research associates. Results: Among the 541 patients, the mean (SD) age was 76.6 (5.2) years, 264 of 540 (49%) were female, and 486 of 540 (90%) were White. Poor prognostic understanding regarding curability was reported for 59% (206 of 348) of patients, and poor prognostic understanding regarding life expectancy estimates was reported for 41% (205 of 496) of patients. Approximately 60% (202 of 336) of patient-oncologist dyads were discordant regarding curability, and 72% (356 of 492) of patient-oncologist dyads were discordant regarding life expectancy estimates. Poor prognostic understanding regarding life expectancy estimates was associated with lower odds of hospice use (adjusted odds ratio, 0.30; 95% CI, 0.16-0.59). Discordance regarding life expectancy estimates was associated with greater odds of hospitalization (adjusted odds ratio, 1.64; 95% CI, 1.01-2.66). Conclusions and Relevance: This study highlights different constructs of prognostic understanding and the need to better understand the association between prognostic understanding and health care use among older adult patients with advanced cancer. Trial Registration: ClinicalTrials.gov Identifier: NCT02107443

SARS-CoV-2 infects the human kidney and drives fibrosis in kidney organoids

Kidney failure is frequently observed during and after COVID-19, but it remains elusive whether this is a direct effect of the virus. Here, we report that SARS-CoV-2 directly infects kidney cells and is associated with increased tubule-interstitial kidney fibrosis in patient autopsy samples. To study direct effects of the virus on the kidney independent of systemic effects of COVID-19, we infected human-induced pluripotent stem-cell-derived kidney organoids with SARS-CoV-2. Single-cell RNA sequencing indicated injury and dedifferentiation of infected cells with activation of profibrotic signaling pathways. Importantly, SARS-CoV-2 infection also led to increased collagen 1 protein expression in organoids. A SARS-CoV-2 protease inhibitor was able to ameliorate the infection of kidney cells by SARS-CoV-2. Our results suggest that SARS-CoV-2 can directly infect kidney cells and induce cell injury with subsequent fibrosis. These data could explain both acute kidney injury in COVID-19 patients and the development of chronic kidney disease in long COVID

Two men with cart horses outside severely damaged buildings after the cyclone, Mackay, Queensland, January 1918 [picture].

Part of collection: Collection of photographs of the destruction caused by the Mackay cyclone, Queensland, January 1918.; Title devised from acquisition documentation.; Condition: Yellowing and discoloration.; Also available in an electronic version via the Internet at: http://nla.gov.au/nla.pic-vn3886559; Purchased 2006

Original Contribution Personality, Socioeconomic Status, and All-Cause Mortality in the United States

The authors assessed the extent to which socioeconomic status (SES) and the personality factors termed the ‘‘big 5’ ’ (neuroticism, extraversion, openness to experience, agreeableness, conscientiousness) represented confounded or independent risks for all-cause mortality over a 10-year follow-up in the Midlife Development in the United States (MIDUS) cohort between 1995 and 2004. Adjusted for demographics, the 25th versus 75th percentile of SES was associated with an odds ratio of 1.43 (95 % confidence interval (CI): 1.11, 1.83). Demographic-adjusted odds ratios for the 75th versus 25th percentile of neuroticism were 1.38 (95 % CI: 1.10, 1.73) and 0.63 (95 % CI: 0.47, 0.84) for conscientiousness, the latter evaluated at high levels of agreeableness. Modest associations were observed between SES and the big 5. Adjusting each for the other revealed that personality explained roughly 20 % of the SES gradient in mortality, while SES explained 8 % of personality risk. Portions of SES and personality risk were explained by health behaviors, although some residual risk remained unexplained. Personality appears to explain some between-SES strata differences in mortality risk, as well as some individual risk heterogeneity within SES strata. Findings suggest that both sociostructural inequalities and individual disposition hold public health implications. Future research and prevention aimed at ameliorating SES health disparities may benefit from considering the risk clustering of social disadvantage and dispositional factors