Cost-Effectiveness of Sleep Apnea Diagnosis and Treatment in Hospitalized Persons With Moderate to Severe Traumatic Brain Injury.

OBJECTIVE: To assess the cost-effectiveness of alternative approaches to diagnose and treat obstructive sleep apnea (OSA) in patients with traumatic brain injury (TBI) during inpatient rehabilitation. SETTING: Data collected during the Comparison of Sleep Apnea Assessment Strategies to Maximize TBI Rehabilitation Participation and Outcome (C-SAS) clinical trial (NCT03033901) on an inpatient rehabilitation TBI cohort were used in this study. STUDY DESIGN: Decision tree analysis was used to determine the cost-effectiveness of approaches to diagnosing and treating sleep apnea. Costs were determined using 2021 Centers for Medicare and Medicaid Services reimbursement codes. Effectiveness was defined in terms of the appropriateness of treatment. Costs averted were extracted from the literature. A sensitivity analysis was performed to account for uncertainty. Analyses were performed for all severity levels of OSA and a subgroup of those with moderate to severe OSA. Six inpatient approaches using various phases of screening, testing, and treatment that conform to usual care or guideline-endorsed interventions were evaluated: (1) usual care; (2) portable diagnostic testing followed by laboratory-quality testing; (3) screening with the snoring, tiredness, observed apnea, high BP, BMI, age, neck circumference, and male gender (STOP-Bang) questionnaire; (4) Multivariable Apnea Prediction Index (MAPI) followed by portable diagnostic testing and laboratory-quality testing; (5) laboratory-quality testing for all; and (6) treatment for all patients. MAIN MEASURES: Cost, Effectiveness, and Incremental Cost-Effectiveness Ratio (ICER). RESULTS: Phased approaches utilizing screening and diagnostic tools were more effective in diagnosing and allocating treatment for OSA than all alternatives in patients with mild to severe and moderate to severe OSA. Usual care was more costly and less effective than all other approaches for mild to severe and moderate to severe OSA. CONCLUSIONS: Diagnosing and treating OSA in patients with TBI is a cost-effective strategy when compared with usual care.

Impact of Overbasalization on Clinical Outcomes in Patients With Type 2 Diabetes: A Post Hoc Analysis of a Large Randomized Controlled Trial.

The American Diabetes Association's Standards of Medical Care in Diabetes emphasize the need for awareness regarding overbasalization (basal insulin doses >0.5 units/kg/day without bolus insulin) in the treatment of type 2 diabetes. However, outcomes data on the impact of overbasalization are limited. This post hoc analysis of a large randomized controlled trial suggests that an insulin therapy regimen involving overbasalization compared with a basal-bolus insulin regimen that avoids overbasalization is less effective at lowering A1C and may be associated with increased cardiovascular risk. Clinicians should consider alternative approaches to glycemic control before increasing basal insulin doses to >0.5 units/kg/day.

Predicting Concussion Recovery with PHQ-9 and GAD-7.

OBJECTIVE: To determine whether scores obtained from Patient Health Questionnaire-9 (PHQ-9) or the General Anxiety Disorder-7 (GAD-7) instruments administered following a concussion can be used to predict recovery time. METHOD: Retrospective cohort study in a university-based specialty concussion center of 502 concussed participants. Participants completed a PHQ-9 and GAD-7 during their initial visit and subsequent visits during the recovery period (ie, at 14, 28, 56, and 84 days). RESULTS: The median recovery time from a concussion was 21 days from the initial clinical evaluation; however, individuals with a PHQ-9 score ≤ 6 (n = 262) had a median recovery time of 17 (95% confidence interval [CI] 15-19) days, whereas those with PHQ-9 scores >6 (n = 240) had a median recovery time of 33 (95% CI 28-37) days and a hazard ratio of 0.525 (95% CI 0.438-0.629, P < 0.0001). For individuals with a GAD-7 score ≤ 4 (n = 259), the median recovery was 19 (95% CI 17-21), days whereas for those with a GAD-7 score > 4 (n = 243), the median recovery was 32 (95% CI 28-36) days with a hazard ratio of 0.554 (95% CI 0.462-0.664, P < 0.00). CONCLUSIONS: Scores obtained from PHQ-9 and GAD-7 screening tools appear to be predictive of an individual's recovery and may help identify those subjects who may benefit from early psychological interventions.

Hypoalbuminemia at Day +90 Is Associated with Inferior Nonrelapse Mortality and Overall Survival in Allogeneic Hematopoietic Cell Transplantation Recipients: A Confirmatory Study.

Prognostic biomarkers in allogeneic hematopoietic cell transplantation (allo-HCT) are needed to improve risk assessment and help guide therapeutic and surveillance strategies to mitigate the risk of death from the procedure. We previously identified hypoalbuminemia at day +90 post-transplantation as an independent predictor of increased nonrelapse mortality (NRM) and inferior overall survival (OS) in patients with acute myelogenous leukemia and myelodysplastic syndrome who were treated with an allo-HCT. Here, we aim to confirm the prognostic significance of day +90 hypoalbuminemia in 783 patients, median age 52 years (range, 18 to 76), who received an allo-HCT for various hematologic malignancies and bone marrow failure syndromes. Multivariate analysis for NRM demonstrated a negative effect of low serum albumin levels (<3.0 versus 3.0 to 3.5 versus >3.5 g/dL) at day +90 post-transplantation (hazard ratios, 8.03 [95% CI, 3.59 to 17.97] versus 2.84 [95% CI, 1.59 to 5.08] versus reference; P < .0001). This was also the case for OS (hazard ratios, 6.86 [95% CI, 4.24 to 11.10] versus 1.52 [95% CI, 1.05 to 2.20] versus reference; P < .0001). Patients with hypoalbuminemia at day +90 post-transplantation are more likely to die from causes other than relapse, particularly infections. This large study confirms the ability of day +90 serum hypoalbuminemia to predict worse NRM and inferior OS. Presence of hypoalbuminemia at day +90 should drive a more rigorous real-time surveillance strategy considering the anticipated high-risk of NRM and poor survival in these patients. Future studies should consider incorporating day +90 serum albumin levels in prognostic models of NRM and OS.

Hypoalbuminaemia segregates different prognostic subgroups within the refined standard risk acute graft-versus-host disease score.

Hypoalbuminaemia has been previously described to predict worse non-relapse mortality (NRM) and inferior overall survival (OS) in allogeneic haematopoietic cell transplant (allo-HCT) recipients. Here, we evaluate the role of hypoalbuminaemia (<35 g/l) at time of onset of acute graft-versus-host disease (aGVHD) when incorporated into the refined aGVHD score. The study population consisted of 522 patients, median age 53 (18-75) years, who underwent an allo-HCT mostly for haematological malignancies. Standard risk (SR) aGVHD comprised 467 patients (89%) and the number of high risk (HR) cases was 55 (11%). Median follow-up for all surviving patients was 26 (3-55) months. Two-year OS was significantly better in patients with SR aGVHD with a serum albumin ≥35 g/l compared to SR with albumin <35 g/l [70% (95% CI = 64-76%) vs. 49% (95% CI = 42-56%), P < 0·0001]. Also, patients with SR aGVHD and a serum albumin level of ≥35 g/l had a significantly lower NRM at 1-year post-transplantation [6% (95% CI = 3-10%) vs. 25% (95% CI = 20-32%), P < 0·0001]. After our findings are validated in a large cohort of patients, we propose that hypoalbuminaemia should be incorporated into the refined aGVHD risk score to further its ability to predict outcomes within this group.

Metformin's impact on statin-associated muscle symptoms: An analysis of ACCORD study data and research materials from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center.

Statins are widely prescribed, yet statin muscle pain limits their use, leading to increased cardiovascular risk. No validated therapy for statin muscle pain exists. The goal of the study was to assess whether metformin was associated with reduced muscle pain. A secondary analysis of data from the ACCORD trial was performed. An ACCORD sub-study assessed patients for muscle cramps and leg/calve pain while walking, typical non-severe statin muscle pain symptoms. We compared muscle pain between patients using a statin (n = 445) or both a statin and metformin (n = 869) at baseline. Overall patient characteristics were balanced between groups. Unadjusted analysis showed fewer reports of muscle cramps (35%) and leg/calve pain while walking (40%) with statins and metformin compared to statin only (muscle cramps, 42%; leg/calve pain while walking, 47%). Multivariable regression demonstrated a 22% odds reduction for muscle cramps (P = 0.049) and a 29% odds reduction for leg/calve pain while walking (P = 0.01). Metformin appears to reduce the risk of non-severe statin muscle pain and additional research is needed to confirm the findings and assess metformin's impact on statin adherence and related cardiovascular outcomes.

Monte Carlo decision curve analysis using aggregate data.

BACKGROUND: Decision curve analysis (DCA) is an increasingly used method for evaluating diagnostic tests and predictive models, but its application requires individual patient data. The Monte Carlo (MC) method can be used to simulate probabilities and outcomes of individual patients and offers an attractive option for application of DCA. MATERIALS AND METHODS: We constructed a MC decision model to simulate individual probabilities of outcomes of interest. These probabilities were contrasted against the threshold probability at which a decision-maker is indifferent between key management strategies: treat all, treat none or use predictive model to guide treatment. We compared the results of DCA with MC simulated data against the results of DCA based on actual individual patient data for three decision models published in the literature: (i) statins for primary prevention of cardiovascular disease, (ii) hospice referral for terminally ill patients and (iii) prostate cancer surgery. RESULTS: The results of MC DCA and patient data DCA were identical. To the extent that patient data DCA were used to inform decisions about statin use, referral to hospice or prostate surgery, the results indicate that MC DCA could have also been used. As long as the aggregate parameters on distribution of the probability of outcomes and treatment effects are accurately described in the published reports, the MC DCA will generate indistinguishable results from individual patient data DCA. CONCLUSIONS: We provide a simple, easy-to-use model, which can facilitate wider use of DCA and better evaluation of diagnostic tests and predictive models that rely only on aggregate data reported in the literature.

When to perform hepatic resection for intermediate-stage hepatocellular carcinoma.

UNLABELLED: Transcatheter arterial chemoembolization (TACE) is the first-line therapy recommended for patients with intermediate hepatocellular carcinoma (HCC). However, in clinical practice, these patients are often referred to surgical teams to be evaluated for hepatectomy. After making a treatment decision (e.g., TACE or surgery), physicians may discover that the alternative treatment would have been preferable, which may bring a sense of regret. Under this premise, it is postulated that the optimal decision will be the one associated with the least amount of regret. Regret-based decision curve analysis (Regret-DCA) was performed on a Cox's regression model developed on 247 patients with cirrhosis resected for intermediate HCC. Physician preferences on surgery versus TACE were elicited in terms of regret; threshold probabilities (Pt) were calculated to identify the probability of survival for which physicians are uncertain of whether or not to perform a surgery. A survey among surgeons and hepatologists regarding three hypothetical clinical cases of intermediate HCC was performed to assess treatment preference domains. The 3- and 5-year overall survival rates after hepatectomy were 48.7% and 33.8%, respectively. Child-Pugh score, tumor number, and esophageal varices were independent predictors of survival (P<0.05). Regret-DCA showed that for physicians with Pt values of 3-year survival between 35% and 70%, the optimal strategy is to rely on the prediction model; for physicians with Pt<35%, surgery should be offered to all patients; and for Pt values>70%, the least regretful strategy is to perform TACE on all patients. The survey showed a significant separation among physicians' preferences, indicating that surgeons and hepatologists can uniformly act according to the regret threshold model. CONCLUSION: Regret theory provides a new perspective for treatment-related decisions applicable to the setting of intermediate HCC.

Rough set theory based prognostic classification models for hospice referral.

BACKGROUND: This paper explores and evaluates the application of classical and dominance-based rough set theory (RST) for the development of data-driven prognostic classification models for hospice referral. In this work, rough set based models are compared with other data-driven methods with respect to two factors related to clinical credibility: accuracy and accessibility. Accessibility refers to the ability of the model to provide traceable, interpretable results and use data that is relevant and simple to collect. METHODS: We utilize retrospective data from 9,103 terminally ill patients to demonstrate the design and implementation RST- based models to identify potential hospice candidates. The classical rough set approach (CRSA) provides methods for knowledge acquisition, founded on the relational indiscernibility of objects in a decision table, to describe required conditions for membership in a concept class. On the other hand, the dominance-based rough set approach (DRSA) analyzes information based on the monotonic relationships between condition attributes values and their assignment to the decision class. CRSA decision rules for six-month patient survival classification were induced using the MODLEM algorithm. Dominance-based decision rules were extracted using the VC-DomLEM rule induction algorithm. RESULTS: The RST-based classifiers are compared with other predictive and rule based decision modeling techniques, namely logistic regression, support vector machines, random forests and C4.5. The RST-based classifiers demonstrate average AUC of 69.74 % with MODLEM and 71.73 % with VC-DomLEM, while the compared methods achieve average AUC of 74.21 % for logistic regression, 73.52 % for support vector machines, 74.59 % for random forests, and 70.88 % for C4.5. CONCLUSIONS: This paper contributes to the growing body of research in RST-based prognostic models. RST and its extensions posses features that enhance the accessibility of clinical decision support models. While the non-rule-based methods-logistic regression, support vector machines and random forests-were found to achieve higher AUC, the performance differential may be outweighed by the benefits of the rule-based methods, particularly in the case of VC-DomLEM. Developing prognostic models for hospice referrals is a challenging problem resulting in substandard performance for all of the evaluated classification methods.

Defining optimum treatment of patients with pancreatic adenocarcinoma using regret-based decision curve analysis.

OBJECTIVE: To use regret decision theory methodology to assess three treatment strategies in pancreatic adenocarcinoma. BACKGROUND: Pancreatic adenocarcinoma is uniformly fatal without operative intervention. Resection can prolong survival in some patients; however, it is associated with significant morbidity and mortality. Regret theory serves as a novel framework linking both rationality and intuition to determine the optimal course for physicians facing difficult decisions related to treatment. METHODS: We used the Cox proportional hazards model to predict survival of patients with pancreatic adenocarcinoma and generated a decision model using regret-based decision curve analysis, which integrates both the patient's prognosis and the physician's preferences expressed in terms of regret associated with a certain action. A physician's treatment preferences are indicated by a threshold probability, which is the probability of death/survival at which the physician is uncertain whether or not to perform surgery. The analysis modeled 3 possible choices: perform surgery on all patients; never perform surgery; and act according to the prediction model. RESULTS: The records of 156 consecutive patients with pancreatic adenocarcinoma were retrospectively evaluated by a single surgeon at a tertiary referral center. Significant independent predictors of overall survival included preoperative stage [P = 0.005; 95% confidence interval (CI), 1.19-2.27], vitality (P < 0.001; 95% CI, 0.96-0.98), daily physical function (P < 0.001; 95% CI, 0.97-0.99), and pathological stage (P < 0.001; 95% CI, 3.06-16.05). Compared with the "always aggressive" or "always passive" surgical treatment strategies, the survival model was associated with the least amount of regret for a wide range of threshold probabilities. CONCLUSIONS: Regret-based decision curve analysis provides a novel perspective for making treatment-related decisions by incorporating the decision maker's preferences expressed as his or her estimates of benefits and harms associated with the treatment considered.

How do physicians decide to treat: an empirical evaluation of the threshold model.

BACKGROUND: According to the threshold model, when faced with a decision under diagnostic uncertainty, physicians should administer treatment if the probability of disease is above a specified threshold and withhold treatment otherwise. The objectives of the present study are to a) evaluate if physicians act according to a threshold model, b) examine which of the existing threshold models [expected utility theory model (EUT), regret-based threshold model, or dual-processing theory] explains the physicians' decision-making best. METHODS: A survey employing realistic clinical treatment vignettes for patients with pulmonary embolism and acute myeloid leukemia was administered to forty-one practicing physicians across different medical specialties. Participants were randomly assigned to the order of presentation of the case vignettes and re-randomized to the order of "high" versus "low" threshold case. The main outcome measure was the proportion of physicians who would or would not prescribe treatment in relation to perceived changes in threshold probability. RESULTS: Fewer physicians choose to treat as the benefit/harms ratio decreased (i.e. the threshold increased) and more physicians administered treatment as the benefit/harms ratio increased (and the threshold decreased). When compared to the actual treatment recommendations, we found that the regret model was marginally superior to the EUT model [Odds ratio (OR) = 1.49; 95% confidence interval (CI) 1.00 to 2.23; p = 0.056]. The dual-processing model was statistically significantly superior to both EUT model [OR = 1.75, 95% CI 1.67 to 4.08; p < 0.001] and regret model [OR = 2.61, 95% CI 1.11 to 2.77; p = 0.018]. CONCLUSIONS: We provide the first empirical evidence that physicians' decision-making can be explained by the threshold model. Of the threshold models tested, the dual-processing theory of decision-making provides the best explanation for the observed empirical results.

Changes in rate-pressure product associated with pregnancy.

BACKGROUND: In nonpregnant individuals, the rate-pressure product, the product of heart rate and systolic blood pressure, is used as a noninvasive surrogate of myocardial O2 consumption during cardiac stress testing. Pregnancy is considered a physiological cardiovascular stress test. Evidence describing the impact of pregnancy on myocardial O2 demand, as assessed by the rate-pressure product, is limited. OBJECTIVE: This study aimed to describe changes in the rate-pressure product for each pregnancy trimester, during labor and delivery, and the postpartum period among low-risk pregnancies. STUDY DESIGN: This was a retrospective cohort study that assessed uncomplicated pregnancies delivered vaginally at term. We collected rate-pressure product (heart rate × systolic blood pressure) values preconception, during pregnancy for each trimester (at ≤13 weeks + 6/7 days, at 14 weeks + 0/7 days through 27 weeks + 6/7 days, and at ≥28 weeks + 0/7 days), during the labor and delivery encounter (hospital admission until complete cervical dilation, complete cervical dilation until placental delivery, and after placental delivery until hospital discharge), and during the outpatient postpartum visit at 2 to 6 weeks after delivery. We calculated the percentage change at each time point from the preconception rate-pressure product (delta rate-pressure product). We used a mixed-linear model to analyze differences in the mean delta rate-pressure product over time and the influence of prepregnancy age, prepregnancy body mass index, and neuraxial anesthesia status during labor and delivery on these estimates. RESULTS: Our cohort comprised 316 patients. The mean rate-pressure product increased significantly from preconception starting at the third trimester of pregnancy and during labor and delivery (P≤.05). The mean delta rate-pressure product peaked at 12% and 38% in the third trimester and during labor and delivery, respectively. Prepregnancy body mass index was inversely correlated with the mean delta rate-pressure product changes (estimate, -0.308; 95% confidence interval, -0.536 to -0.80; P=.008). In contrast, neither the prepregnancy age, nor neuraxial anesthesia status during labor had a significant influence on this parameter. CONCLUSION: This study validates the transient but significant increase in the rate-pressure product, a clinical estimate of myocardial O2 demand, during uncomplicated pregnancies delivered vaginally at term. Pregnant individuals with lower prepregnancy body mass index experienced a sharper increase in this parameter. Patients who receive neuraxial anesthesia during labor and delivery experience similar changes in the rate-pressure product as those who did not.

Telemedicine appointments are more likely to be completed than in-person healthcare appointments: a retrospective cohort study.

Objectives: Missed appointments can lead to treatment delays and adverse outcomes. Telemedicine may improve appointment completion because it addresses barriers to in-person visits, such as childcare and transportation. This study compared appointment completion for appointments using telemedicine versus in-person care in a large cohort of patients at an urban academic health sciences center. Materials and Methods: We conducted a retrospective cohort study of electronic health record data to determine whether telemedicine appointments have higher odds of completion compared to in-person care appointments, January 1, 2021, and April 30, 2023. The data were obtained from the University of South Florida (USF), a large academic health sciences center serving Tampa, FL, and surrounding communities. We implemented 1:1 propensity score matching based on age, gender, race, visit type, and Charlson Comorbidity Index (CCI). Results: The matched cohort included 87 376 appointments, with diverse patient demographics. The percentage of completed telemedicine appointments exceeded that of completed in-person care appointments by 9.2 points (73.4% vs 64.2%, P < .001). The adjusted odds ratio for telemedicine versus in-person care in relation to appointment completion was 1.64 (95% CI, 1.59-1.69, P < .001), indicating that telemedicine appointments are associated with 64% higher odds of completion than in-person care appointments when controlling for other factors. Discussion: This cohort study indicated that telemedicine appointments are more likely to be completed than in-person care appointments, regardless of demographics, comorbidity, payment type, or distance. Conclusion: Telemedicine appointments are more likely to be completed than in-person healthcare appointments.

A Reproducible Model Based on Clinical Text for Predicting Suicidal Behavior.

Suicide risk models are critical for prioritizing patients for intervention. We demonstrate a reproducible approach for training text classifiers to identify patients at risk. The models were effective in phenotyping suicidal behavior (F1=.94) and moderately effective in predicting future events (F1=.63).

A US Multicenter Collaborative Study on Outcomes of Hematopoietic Cell Transplantation in Hepatosplenic T-Cell Lymphoma.

Hepatosplenic T-cell lymphoma (HSTCL) is a rare and aggressive type of peripheral T-cell lymphoma with median overall survival (OS) of approximately 1 year. Data on the effectiveness of hematopoietic cell transplantation (HCT) is limited, as is the choice between autologous HCT (auto-HCT) and allogeneic HCT (allo-HCT) in the treatment of this disease. To evaluate the outcome of patients with HSTCL who underwent either auto-HCT or allo-HCT, we performed a multi-institutional retrospective cohort study to assess outcomes of HCT in HSTCL patients. Fifty-three patients with HSTCL were included in the study. Thirty-six patients received an allo-HCT and 17 received an auto-HCT. Thirty-five (66%) were males. Median age at diagnosis was 38 (range 2 to 64) years. Median follow-up for survivors was 75 months (range 8 to 204). The median number of prior lines of therapy was 1 (range 1 to 4). Median OS and progression-free survival (PFS) for the entire cohort were 78.5 months (95% CI: 25 to 79) and 54 months (95% CI: 18 to 75), respectively. There were no significant differences in OS (HR: 0.63, 95% CI: 0.28 to 1.45, P = .245) or PFS (HR: 0.7, 95% CI: 0.32 to 1.57, P = .365) between the allo-HCT and auto-HCT groups, respectively. In the allo-HCT group, the 3-year cumulative incidence of relapse was 35% (95% CI: 21 to 57), while 3-year cumulative incidence of NRM was 16% (95% CI: 7 to 35). In the auto-HCT group, the 3-year cumulative incidence of relapse and NRM were 43% (95% CI: 23 to 78) and 14% (95% CI: 4 to 52), respectively. Both Auto-HCT and Allo-HCT are effective consolidative strategies in patients with HSTCL, and patients should be promptly referred for HCT evaluation.

Direct Versus Indirect Query Performance of ICD-9/-10 Coding to Identify Anaphylaxis.

BACKGROUND: Anaphylaxis is an often under =diagnosed, severe allergic event for which epidemiological data are sporadic. Researchers have leveraged administrative and claims data algorithms to study large databases of anaphylactic events; however, little longitudinal data analysis is available after transition to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM). OBJECTIVE: Study longitudinal trends in anaphylaxis incidence using direct and indirect query methods. METHODS: Emergency department (ED) and inpatient data were analyzed from a large state health care administration database from 2011 to 2020. Incidence was calculated using direct queries of anaphylaxis ICD-9-CM and ICD-10-CM codes and indirect queries using a symptom-based ICD-9-CM algorithm and forward mapped ICD-10-CM version to identify undiagnosed anaphylaxis episodes and to assess algorithm performance at the population level. RESULTS: An average of 2.4 million inpatient and 7.5 million ED observations/y were analyzed. Using the direct query method, annual ED anaphylaxis cases increased steadily from 1,454 (2011) to 4,029 (2019) then declined to 3,341 in 2020 during the coronavirus disease 2019 (COVID-19) pandemic. In contrast, inpatient cases remained relatively steady, with a slight decline after 2015 during the ICD version transition, until a significant drop occurred in 2020. Using the indirect queries, anaphylaxis cases increased markedly after the ICD transition year, especially involving drug-related anaphylaxis. CONCLUSIONS: Nontypical drug associations with anaphylaxis episodes using the ICD-10-CM version of the algorithm suggest poor performance with drug-related codes. Further, the increased granularity of ICD-10-CM identified potential limitations of a previously validated symptom-based ICD-9-CM algorithm used to detect undiagnosed cases.

Poor Oral Health Linked with Higher Risk of Alzheimer's Disease.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disease characterized by cognitive and behavioral changes in older adults. Emerging evidence suggests poor oral health is associated with AD, but there is a lack of large-scale clinical studies demonstrating this link. Herein, we used the TriNetX database to generate clinical cohorts and assess the risk of AD and survival among >30 million de-identified subjects with normal oral health (n = 31,418,814) and poor oral health (n = 1,232,751). There was a greater than two-fold increase in AD risk in the poor oral health cohort compared to the normal oral health group (risk ratio (RR): 2.363, (95% confidence interval: 2.326, 2.401)). To reduce potential bias, we performed retrospective propensity score matching for age, gender, and multiple laboratory measures. After matching, the cohorts had no significant differences in survival probability. Furthermore, when comparing multiple oral conditions, diseases related to tooth loss were the most significant risk factor for AD (RR: 3.186, (95% CI: 3.007, 3.376)). Our results suggest that oral health may be important in AD risk, regardless of age, gender, or laboratory measures. However, more large-scale cohort studies are necessary to validate these findings and further evaluate links between oral health and AD.

Regret affects the choice between neoadjuvant therapy and upfront surgery for potentially resectable pancreatic cancer.

BACKGROUND: When treating potentially resectable pancreatic adenocarcinoma, therapeutic decisions are left to the sensibility of treating clinicians who, faced with a decision that post hoc can be proven wrong, may feel a sense of regret that they want to avoid. A regret-based decision model was applied to evaluate attitudes toward neoadjuvant therapy versus upfront surgery for potentially resectable pancreatic adenocarcinoma. METHODS: Three clinical scenarios describing high-, intermediate-, and low-risk disease-specific mortality after upfront surgery were presented to 60 respondents (20 oncologists, 20 gastroenterologists, and 20 surgeons). Respondents were asked to report their regret of omission and commission regarding neoadjuvant chemotherapy on a scale between 0 (no regret) and 100 (maximum regret). The threshold model and a multilevel mixed regression were applied to analyze respondents' attitudes toward neoadjuvant therapy. RESULTS: The lowest regret of omission was elicited in the low-risk scenario, and the highest regret in the high-risk scenario (P < .001). The regret of the commission was diametrically opposite to the regret of omission (P ≤ .001). The disease-specific threshold mortality at which upfront surgery is favored over the neoadjuvant therapy progressively decreased from the low-risk to the high-risk scenarios (P ≤ .001). The nonsurgeons working in or with lower surgical volume centers (P = .010) and surgeons (P = .018) accepted higher disease-specific mortality after upfront surgery, which resulted in the lower likelihood of adopting neoadjuvant therapy. CONCLUSION: Regret drives decision making in the management of pancreatic adenocarcinoma. Being a surgeon or a specialist working in surgical centers with lower patient volumes reduces the likelihood of recommending neoadjuvant therapy.

Dual processing model of medical decision-making.

BACKGROUND: Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease. METHODS: We developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice. RESULTS: We show that physician's beliefs about whether to treat at higher (lower) probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker's threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice. CONCLUSIONS: We have developed the first dual processing model of medical decision-making that has potential to enrich the current medical decision-making field, which is still to the large extent dominated by expected utility theory. The model also provides a platform for reconciling two groups of competing dual processing theories (parallel competitive with default-interventionalist theories).