Postoperative outcomes and costs of laparoscopic versus robotic distal pancreatectomy: a propensity-matched analysis.

BACKGROUND: Minimally invasive distal pancreatectomy (MIDP) has established advantages over the open approach. The costs associated with robotic DP (RDP) versus laparoscopic DP (LDP) make the robotic approach controversial. We sought to compare outcomes and cost of LDP and RDP using propensity matching analysis at our institution. METHODS: Patients undergoing LDP or RDP between 2000 and 2021 were retrospectively identified. Patients were optimally matched using age, gender, American Society of Anesthesiologists status, body mass index, and tumor size. Between-group differences were analyzed using the Wilcoxon signed-rank test for continuous data, and the McNemar's test for categorical data. Outcomes included operative duration, conversion to open surgery, postoperative length of stay, pancreatic fistula rate, pseudocyst requiring intervention, and costs. RESULTS: 298 patients underwent MIDP, 180 (60%) were laparoscopic and 118 (40%) were robotic. All RDPs were matched 1:1 to a laparoscopic case with absolute standardized mean differences for all matching covariates below 0.10, except for tumor type (0.16). RDP had longer operative times (268 vs 178 min, p < 0.01), shorter length of stay (2 vs 4 days, p < 0.01), fewer biochemical pancreatic leaks (11.9% vs 34.7%, p < 0.01), and fewer interventional radiological drainage (0% vs 5.9%, p = 0.01). The number of pancreatic fistulas (11.9% vs 5.1%, p = 0.12), collections requiring antibiotics or intervention (11.9% vs 5.1%, p = 0.12), and conversion rates (3.4% vs 5.1%, p = 0.72) were comparable between the two groups. The total direct index admission costs for RDP were 1.01 times higher than for LDP for FY16-19 (p = 0.372), and 1.33 times higher for FY20-22 (p = 0.031). CONCLUSIONS: Although RDP required longer operative times than LDP, postoperative stays were shorter. The procedure cost of RDP was modestly more expensive than LDP, though this was partially offset by reduced hospital stay and reintervention rate.

Sociodemographic differences in use of nicotine, cannabis, and non-nicotine E-cigarette devices.

BACKGROUND: Sociodemographic differences in e-cigarette use have been documented; however, disparities in use of specific e-cigarette types with various ingredients have yet to be thoroughly investigated. This study examines ever- and past-30-day-use of nicotine, cannabis, and non-nicotine e-cigarette device types by sex, sexual orientation, race/ethnicity, and financial comfort. METHODS: Data were drawn from a 2021 national cross-sectional survey of adolescents, young adults, and adults (N = 6131, ages 13-40 years old). Participants reported ever and past-30-day-use of (1) disposable nicotine e-cigarettes, (2) pod/cartridge-based nicotine e-cigarettes, (3) "other" nicotine e-cigarettes, (4) non-nicotine e-cigarettes, (5) e-cigarettes with THC, and (6) e-cigarettes with CBD. We constructed summary tables for each e-cigarette device type in which percentages of ever and past-30-day-use were calculated by birth year category and sociodemographic variables: (a) sex, (b) sexual orientation, (c) race/ethnicity, and (d) financial comfort. RESULTS: Females born between 1996 and 2008 reported higher rates of past-30-day disposable e-cigarette use relative to males (females 26.4%; males 22.4%). Compared to their heterosexual counterparts, LGBTQ+ participants reported higher overall rates of past-30-day-use for disposable (LGBTQ+ 27.9%; Heterosexual 23.8%), THC (LGBTQ+ 30.8%; Heterosexual 24.1%), and CBD e-cigarettes (LGBTQ+ 20.0%; Heterosexual 16.9%). Hispanic/Latino participants generally reported higher rates of past-30-day-use across device types relative to those identifying as Black or White non-Hispanic, particularly disposable nicotine e-cigarettes. CONCLUSIONS: Findings highlight sociodemographic disparities in e-cigarette use, though differences varied based on e-cigarette device type and participant birth year category. Tailored preventive efforts may be necessary to mitigate e-cigarette use among populations at highest risk.

Feedback Loop Failure Modes in Medical Diagnosis: How Biases Can Emerge and Be Reinforced.

BACKGROUND: Medical diagnosis in practice connects to research through continuous feedback loops: Studies of diagnosed cases shape our understanding of disease, which shapes future diagnostic practice. Without accounting for an imperfect and complex diagnostic process in which some cases are more likely to be diagnosed correctly (or diagnosed at all), the feedback loop can inadvertently exacerbate future diagnostic errors and biases. FRAMEWORK: A feedback loop failure occurs if misleading evidence about disease etiology encourages systematic errors that self-perpetuate, compromising future diagnoses and patient care. This article defines scenarios for feedback loop failure in medical diagnosis. DESIGN: Through simulated cases, we characterize how disease incidence, presentation, and risk factors can be misunderstood when observational data are summarized naive to biases arising from diagnostic error. A fourth simulation extends to a progressive disease. RESULTS: When severe cases of a disease are diagnosed more readily, less severe cases go undiagnosed, increasingly leading to underestimation of the prevalence and heterogeneity of the disease presentation. Observed differences in incidence and symptoms between demographic groups may be driven by differences in risk, presentation, the diagnostic process itself, or a combination of these. We suggested how perceptions about risk factors and representativeness may drive the likelihood of diagnosis. Differing diagnosis rates between patient groups can feed back to increasingly greater diagnostic errors and disparities in the timing of diagnosis and treatment. CONCLUSIONS: A feedback loop between past data and future medical practice may seem obviously beneficial. However, under plausible scenarios, poorly implemented feedback loops can degrade care. Direct summaries from observational data based on diagnosed individuals may be misleading, especially concerning those symptoms and risk factors that influence the diagnostic process itself. HIGHLIGHTS: Current evidence about a disease can (and should) influence the diagnostic process. A feedback loop failure may occur if biased "evidence" encourages diagnostic errors, leading to future errors in the evidence base.When diagnostic accuracy varies for mild versus severe cases or between demographic groups, incorrect conclusions about disease prevalence and presentation will result without specifically accounting for such variability.Use of demographic characteristics in the diagnostic process should be done with careful justification, in particular avoiding potential cognitive biases and overcorrection.