Narrow Results

The objective of this research was to build and assess the performance of a prediction model for post-operative recovery status measured by quality of life among individuals experiencing a variety of surgery types. In addition, we assessed the performance of the model for two subgroups (high and moderately consistent wearable device users). Study variables were derived from the electronic health records, questionnaires, and wearable devices of a cohort of individuals with one of 8 surgery types and that were part of the NIH All of Us research program. Through multivariable analysis, high frailty index (OR 1.69, 95% 1.05-7.22, p<0.006), and older age (OR 1.76, 95% 1.55-4.08, p<0.024) were found to be the driving risk factors of poor recovery post-surgery. Our logistic regression model included 15 variables, 5 of which included wearable device data. In wearable use subgroups, the model had better accuracy for high wearable users (81%). Findings demonstrate the potential for models that use wearable measures to assess frailty to inform clinicians of patients at risk for poor surgical outcomes. Our model performed with high accuracy across multiple surgery types and were robust to variable consistency in wearable use.

This PSB 2023 session discusses challenges in clinical implication and application of risk prediction models, which includes but is not limited to: implementation of risk models, responsible use of polygenic risk scores (PGS), and other risk prediction strategies. We focus on the development and use of new, scalable methods for harmonizing and refining risk prediction models by incorporating genetic and non-genetic risk factors, applying new phenotyping strategies, and integrating clinical factors and biomarkers. Lastly, we will discuss innovation in expanding the utility of these prediction models to underrepresented populations. This session focuses on the overarching theme of enabling early diagnosis, and treatment and preventive measures related to complex diseases and comorbidities.

The socio-economic risk factors predisposing diabetics to foot problems were studied prospectively using two cohorts — one cohort consisting of 122 patients with diabetic foot problems (including cellulitis, abscess, osteomyelitis, septic arthritis, gangrene, ulcers and Charcot joint disease) and another cohort of 112 diabetics without foot problems seen in National University Hospital, from January to April 2007. A detailed protocol was designed. Factors studied included patient profile, education level, average monthly household income, compliance to diabetic medication, attendance at clinics for diabetic treatment, smoking, alcohol consumption, frequency of exercise and HbA1C level. Risk factors were determined by univariate and stepwise logistic regression analysis. Our results showed that with multivariate analysis, significant risk factors predisposing to diabetic foot problems were Malays (p < 0.001), education level of up to secondary school only (p = 0.021), low average monthly household income of less than S$2000 (US$1333) (p = 0.030), lack of weekly exercise (at least once a week, p = 0.04) and high HbA1C level (>7.0%, p = 0.015).

Five hundred and fifty-seven patients who received diabetic foot screening services at the National University Hospital (NUH) from June 2006 to June 2007 were evaluated. A standardised protocol was used. All patients were classified according to King's classification. For neurological assessment, monofilament testing and biothesiometer testing were used. For vascular assessment, ankle brachial index (ABI), toe brachial index (TBI) and the presence or absence of palpable pulses inthe foot were used. The foot screening was performed by a trained assistant nurse. Majority of the patients were in the fifth (25.0%), sixth (26.7%) and seventh (25.0%) decades of life. The male:female ratio was 1.1:1; 524 had type II diabetes; and only 33 had type I diabetes. Theaverage duration of diabetes was 11.4 years. Hypertension (74.1%) was the most common co-morbidity followed by retinopathy (43.4%), IHD (35.4%), nephropathy (25.8%) and stroke (11.3%). Neuropathy was found in 225 (40.4%) patients based on 5.07 Semmes-Weinsteinmonofilament testing. Peripheral vascular disease (PVD) was recorded in 51 (4.6%) and 251 (22.5%) feet based on ABI < 0.8 and TBI < 0.7. Risk factors included hyperlipidaemia (67.1%) and smoking (11.5%). According to King's classification, 217 (39.0%) were Stage 1: Normal, 301 (54.0%) were Stage 2: High-Risk, 17 (4.8%) were Stage 3: Ulcerated, and 12 (2.2%) were Stage 4: Cellulitic. Foot screening is useful in the care of diabetic patients. Risk factors could be detected early for prompt referral to the relevant disciplines to prevent diabetic foot complications.

Post traumatic stress disorder (PTSD) are emergent phenomena resulting from exposure to a traumatic event that causes actual or threatened death or injury and produces intense fear, helplessness, or horror. In order to assess the role of different factors contributing to this kind of emergent phenomenon prevalence rates across gender, cultures, and samples exposed to different traumas are examined. Risk factors for PTSD, including pre-existing individual-based factors, features of the traumatic event, and post-trauma interventions are examined as well. Several characteristics of the trauma, related to cognitions, post-trauma social support and therapeutic interventions for PTSD are also considered. Further work is needed in order to analyze the inter-relationships among these factors and underlying mechanisms. The chaotic nature of traumatic processes, the multiple and interactive impacts on traumatic events require a comprehensive perspective aimed at planning effective interventions. Treatment outcome studies recommended the combined use of training and therapies as first-line treatment for PTSD.

In order to quantitatively describe the risk of well control in drilling, the risk factors and weight assignment were evaluated by expert scoring method and analytic hierarchy process (AHP). The evaluation system of first levels of 4 factors, such as geological uncertainty, well control equipment, process operation, personnel quality, etc., is established, and the evaluation system of second levels and multiple factors is established. Evaluation results show that geological uncertainty is caused by a primary factor in the well control risk, the oilfield exploration well control risk status as “great risk”. The application results show that the method is feasible for evaluating well control risk.

In this chapter, we discuss a very important topic about arbitrage pricing theory and the connection to multi-factor asset pricing models. The latter is at the heart of many quantitative investment strategies aimed at portfolio selection and rebalancing to capture positive alphas and hence above market returns. Application of White's heteroskedasticity consistent covariance matrix estimator is also discussed.