Celiac plexus neurolysis (CPN) is currently used as salvage therapy for morphine-resistant pancreatic cancer pain. Endoscopic ultrasound-guided CPN (EUS-CPN) can be performed early, at the time of ...EUS. We hypothesized that early EUS-CPN would reduce pain and morphine consumption, increase quality of life (QOL), and prolong survival.
Patients were eligible if referred for EUS for suspected pancreatic cancer with related pain. If EUS and EUS-guided fine-needle aspiration cytology confirmed inoperable adenocarcinoma, patients were randomly assigned to early EUS-CPN or conventional pain management. Pain scores (7-point Likert scale), morphine equivalent consumption, and QOL scores (Digestive Disease Questionnaire-15) were assessed at 1 and 3 months.
Five hundred eighty eligible patients were seen between April 2006 and December 2008. Ninety-six patients were randomly assigned (48 patients per study arm). Pain relief was greater in the EUS-CPN group at 1 month and significantly greater at 3 months (difference in mean percent change in pain score = -28.9 95% CI, -67.0 to 2.8, P = .09, and -60.7 95% CI, -86.6 to -25.5, P = .01, respectively). Morphine consumption was similar in both groups at 1 month (difference in mean change in morphine consumption = -1.0 95% CI, -47.7 to 49.2, P = .99), but tended toward lower consumption at 3 months in the neurolysis group (difference in mean change in morphine consumption = -49.5 95% CI, -127.5 to 7.0, P = .10). There was no effect on QOL or survival.
Early EUS-CPN reduces pain and may moderate morphine consumption in patients with painful, inoperable pancreatic adenocarcinoma. EUS-CPN can be considered in all such patients at the time of diagnostic and staging EUS.
Improved mortality prediction for patients in intensive care units is a big challenge. Many severity scores have been proposed, but findings of validation studies have shown that they are not ...adequately calibrated. The Super ICU Learner Algorithm (SICULA), an ensemble machine learning technique that uses multiple learning algorithms to obtain better prediction performance, does at least as well as the best member of its library. We aimed to assess whether the Super Learner could provide a new mortality prediction algorithm for patients in intensive care units, and to assess its performance compared with other scoring systems.
From January, 2001, to December, 2008, we used the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database (version 26) including all patients admitted to an intensive care unit at the Beth Israel Deaconess Medical Centre, Boston, MA, USA. We assessed the calibration, discrimination, and risk classification of predicted hospital mortality based on Super Learner compared with SAPS-II, APACHE-II, and SOFA. We calculated performance measures with cross-validation to avoid making biased assessments. Our proposed score was then externally validated on a dataset of 200 randomly selected patients admitted at the intensive care unit of Hôpital Européen Georges-Pompidou, Paris, France, between Sept 1, 2013, and June, 30, 2014. The primary outcome was hospital mortality. The explanatory variables were the same as those included in the SAPS II score.
24,508 patients were included, with median SAPS-II of 38 (IQR 27-51) and median SOFA of 5 (IQR 2-8). 3002 of 24,508 (12%) patients died in the Beth Israel Deaconess Medical Centre. We produced two sets of predictions based on the Super Learner; the first based on the 17 variables as they appear in the SAPS-II score (SL1), and the second, on the original, untransformed variables (SL2). The two versions yielded average predicted probabilities of death of 0·12 (IQR 0·02-0·16) and 0·13 (0·01-0·19), whereas the corresponding value for SOFA was 0·12 (0·05-0·15) and for SAPS-II 0·30 (0·08-0·48). The cross-validated area under the receiver operating characteristic curve (AUROC) for SAPS-II was 0·78 (95% CI 0·77-0·78) and 0·71 (0·70-0·72) for SOFA. Super Learner had an AUROC of 0·85 (0·84-0·85) when the explanatory variables were categorised as in SAPS-II, and of 0·88 (0·87-0·89) when the same explanatory variables were included without any transformation. Additionally, Super Learner showed better calibration properties than previous score systems. On the external validation dataset, the AUROC was 0·94 (0·90-0·98) and calibration properties were good.
Compared with conventional severity scores, Super Learner offers improved performance for predicting hospital mortality in patients in intensive care units. A user-friendly implementation is available online and should be useful for clinicians seeking to validate our score.
Fulbright Foundation, Assistance Publique-Hôpitaux de Paris, Doris Duke Clinical Scientist Development Award, and the NIH.
Objective:
Transplant recipients are at risk of developing progressive multifocal leukoencephalopathy (PML), a rare demyelinating disorder caused by oligodendrocyte destruction by JC virus.
Methods:
...Reports of PML following transplantation were found using PubMed Entrez (1958–July 2010). A multicenter, retrospective cohort study also identified all cases of PML among transplant recipients diagnosed at Mayo Clinic, Johns Hopkins University, Washington University, and Amsterdam Academic Medical Center. At 1 institution, the incidence of posttransplantation PML was calculated.
Results:
A total of 69 cases (44 solid organ, 25 bone marrow) of posttransplantation PML were found including 15 from the 4 medical centers and another 54 from the literature. The median time to development of first symptoms of PML following transplantation was longer in solid organ vs bone marrow recipients (27 vs 11 months, p = 0.0005, range of <1 to >240). Median survival following symptom onset was 6.4 months in solid organ vs 19.5 months in bone marrow recipients (p = 0.068). Case fatality was 84% (95% confidence interval CI, 70.3–92.4%) and survival beyond 1 year was 55.7% (95% CI, 41.2–67.2%). The incidence of PML among heart and/or lung transplant recipients at 1 institution was 1.24 per 1,000 posttransplantation person‐years (95% CI, 0.25–3.61). No clear association was found with any 1 immunosuppressant agent. No treatment provided demonstrable therapeutic benefit.
Interpretation:
The risk of PML exists throughout the posttransplantation period. Bone marrow recipients survive longer than solid organ recipients but may have a lower median time to first symptoms of PML. Posttransplantation PML has a higher case fatality and may have a higher incidence than reported in human immunodeficiency virus (HIV) patients on highly‐active antiretroviral therapy (HAART) or multiple sclerosis patients treated with natalizumab. ANN NEUROL 2011;
Estimation and evaluation of individualized treatment rules have been studied extensively, but real-world treatment resource constraints have received limited attention in existing methods. We ...investigate a setting in which treatment is intervened upon based on covariates to optimize the mean counterfactual outcome under treatment cost constraints when the treatment cost is random. In a particularly interesting special case, an instrumental variable corresponding to encouragement to treatment is intervened upon with constraints on the proportion receiving treatment. For such settings, we first develop a method to estimate optimal individualized treatment rules. We further construct an asymptotically efficient plug-in estimator of the corresponding average treatment effect relative to a given reference rule.
People experiencing homelessness (PEH) are at increased risk for acquiring SARS-CoV-2, but the burden of long COVID in this population is unknown.
We conducted a matched prospective cohort study to ...assess the prevalence, characteristics, and impact of long COVID among sheltered PEH in Seattle, WA between September 2020-April 2022. Adults ≥ 18 years, residing across nine homeless shelters with active respiratory virus surveillance, were eligible to complete in-person baseline surveys and interval follow-up phone surveys. We included a subset of 22 COVID-19-positive cases who tested positive or inconclusive for SARS-CoV-2 and 44 COVID-19-negative controls who tested negative for SARS-CoV-2, frequency matched on age and sex. Among controls, 22 were positive and 22 were negative for one of 27 other respiratory virus pathogens. To assess the impact of COVID-19 on the risk of symptom presence at follow-up (day 30-225 post-enrollment test), we performed log-linear regression with robust standard errors, adjusting for confounding by shelter site and demographic variables determined a priori.
Of 53 eligible COVID-19 cases, 22 (42%) completed ≥ 1 follow-up survey. While five (23%) cases reported ≥ 1 symptom at baseline, this increased to 77% (10/13) between day 30-59 and 33% (4/12) day 90 + . The most commonly reported symptoms day 30 + were fatigue (27%) and rhinorrhea (27%), with 8 (36%) reporting symptoms that interfered with or prevented daily activities. Four (33%) symptomatic cases reported receiving medical care outside of a medical provider at an isolation facility. Of 44 controls, 12 (27%) reported any symptoms day 90 + . Risk of any symptoms at follow-up was 5.4 times higher among COVID-19 cases compared to controls (95% CI: 2.7-10.5).
Shelter residents reported a high prevalence of symptoms 30 + days after their SARS-CoV-2 detection, though few accessed medical care for persistent illness. The impact of COVID-19 extends beyond acute illness and may exacerbate existing challenges that marginalized populations face in maintaining their health and wellbeing.
•Focus: Dementia incidence and exposure to traffic-related air pollution (TRAP).•Study design: Community-based prospective cohort study in Seattle, WA, age 65+.•Exposure: predicted UFP, BC, and NO2 ...from an extensive monitoring campaign.•Result: No evidence of a greater hazard of dementia incidence with TRAP exposures.•Conclude: Sensitivity and secondary analyses were in agreement; replication needed.
While epidemiologic evidence links higher levels of exposure to fine particulate matter (PM2.5) to decreased cognitive function, fewer studies have investigated links with traffic-related air pollution (TRAP), and none have examined ultrafine particles (UFP, ≤100 nm) and late-life dementia incidence.
To evaluate associations between TRAP exposures (UFP, black carbon BC, and nitrogen dioxide NO2) and late-life dementia incidence.
We ascertained dementia incidence in the Seattle-based Adult Changes in Thought (ACT) prospective cohort study (beginning in 1994) and assessed ten-year average TRAP exposures for each participant based on prediction models derived from an extensive mobile monitoring campaign. We applied Cox proportional hazards models to investigate TRAP exposure and dementia incidence using age as the time axis and further adjusting for sex, self-reported race, calendar year, education, socioeconomic status, PM2.5, and APOE genotype. We ran sensitivity analyses where we did not adjust for PM2.5 and other sensitivity and secondary analyses where we adjusted for multiple pollutants, applied alternative exposure models (including total and size-specific UFP), modified the adjustment covariates, used calendar year as the time axis, assessed different exposure periods, dementia subtypes, and others.
We identified 1,041 incident all-cause dementia cases in 4,283 participants over 37,102 person-years of follow-up. We did not find evidence of a greater hazard of late-life dementia incidence with elevated levels of long-term TRAP exposures. The estimated hazard ratio of all-cause dementia was 0.98 (95 % CI: 0.92–1.05) for every 2000 pt/cm3 increment in UFP, 0.95 (0.89–1.01) for every 100 ng/m3 increment in BC, and 0.96 (0.91–1.02) for every 2 ppb increment in NO2. These findings were consistent across sensitivity and secondary analyses.
We did not find evidence of a greater hazard of late-life dementia risk with elevated long-term TRAP exposures in this population-based prospective cohort study.
Dementia is one of the world's major public health challenges. The lifetime risk of dementia is the proportion of individuals who ever develop dementia during their lifetime. Despite its importance ...to epidemiologists and policy-makers, this measure does not seem to have been estimated in the Canadian population. Data from a birth cohort study of dementia are not available. Instead, we must rely on data from the Canadian Study of Health and Aging, a large cross-sectional study of dementia with follow-up for survival. These data present challenges because they include substantial loss to follow-up and are not representatively drawn from the target population because of structural sampling biases. A first bias is imparted by the cross-sectional sampling scheme, while a second bias is a result of stratified sampling. Estimation of the lifetime risk and related quantities in the presence of these biases has not been previously addressed in the literature. We develop and study nonparametric estimators of the lifetime risk, the remaining lifetime risk, and cumulative risk at specific ages, accounting for these complexities. In particular, we reveal the fact that estimation of the lifetime risk is invariant to stratification by current age at sampling. We present simulation results validating our methodology, and provide novel facts about the epidemiology of dementia in Canada using data from the Canadian Study of Health and Aging. Supplementary materials for this article are available online.
Overall, resilient health systems build upon sufficient, qualified, well-distributed, and motivated health workers; however, this precious resource is limited in numbers to meet people's demands, ...particularly in LMICs. Understanding the subnational distribution of health workers from different lens is critical to ensure quality healthcare and improving health outcomes.
Using data from Health Personnel Information System, facility-level Service Availability and Readiness Assessment, and other sources, we performed a district-level longitudinal analysis to assess health workforce density and the ratio of male to female health workers between January 2016 and June 2020 across all districts in Mozambique.
22 011 health workers were sampled, of whom 10 405 (47.3%) were male. The average age was 35 years (SD: 9.4). Physicians (1025, 4.7%), maternal and child health nurses (4808, 21.8%), and nurses (6402, 29.1%) represented about 55% of the sample. In January 2016, the average district-level workforce density was 75.8 per 100 000 population (95% CI 65.9, 87.1), and was increasing at an annual rate of 8.0% (95% CI 6.00, 9.00) through January 2018. The annual growth rate declined to 3.0% (95% CI 2.00, 4.00) after January 2018. Two provinces, Maputo City and Maputo Province, with 268.3 (95% CI 186.10, 387.00) and 104.6 (95% CI 84.20, 130.00) health workers per 100 000 population, respectively, had the highest workforce density at baseline (2016). There were 3122 community health workers (CHW), of whom 72.8% were male, in January 2016. The average number of CHWs per 10 000 population was 1.33 (95% CI 1.11, 1.59) in 2016 and increased by 18% annually between January 2016 and January 2018. This trend reduced to 11% (95% CI 0.00, 13.00) after January 2018. The sex ratio was twice as high for all provinces in the central and northern regions relative to Maputo Province. Maputo City (OR: 0.34; 95% CI 0.32, 0.34) and Maputo Province (OR: 0.56; 95% CI 0.49, 0.65) reported the lowest sex ratio at the baseline. Encouragingly, important sex ratio improvements were observed after January 2018, particularly in the northern and central regions.
Mozambique made substantial progress in health workers' availability during the study period; however, with a critical slowdown after 2018. Despite the progress, meaningful shortages and distribution disparities persist.
The broadly neutralizing antibody (bnAb) VRC01 is being evaluated for its efficacy to prevent HIV-1 infection in the Antibody Mediated Prevention (AMP) trials. A secondary objective of AMP utilizes ...sieve analysis to investigate how VRC01 prevention efficacy (PE) varies with HIV-1 envelope (Env) amino acid (AA) sequence features. An exhaustive analysis that tests how PE depends on every AA feature with sufficient variation would have low statistical power. To design an adequately powered primary sieve analysis for AMP, we modeled VRC01 neutralization as a function of Env AA sequence features of 611 HIV-1 gp160 pseudoviruses from the CATNAP database, with objectives: (1) to develop models that best predict the neutralization readouts; and (2) to rank AA features by their predictive importance with classification and regression methods. The dataset was split in half, and machine learning algorithms were applied to each half, each analyzed separately using cross-validation and hold-out validation. We selected Super Learner, a nonparametric ensemble-based cross-validated learning method, for advancement to the primary sieve analysis. This method predicted the dichotomous resistance outcome of whether the IC50 neutralization titer of VRC01 for a given Env pseudovirus is right-censored (indicating resistance) with an average validated AUC of 0.868 across the two hold-out datasets. Quantitative log IC50 was predicted with an average validated R2 of 0.355. Features predicting neutralization sensitivity or resistance included 26 surface-accessible residues in the VRC01 and CD4 binding footprints, the length of gp120, the length of Env, the number of cysteines in gp120, the number of cysteines in Env, and 4 potential N-linked glycosylation sites; the top features will be advanced to the primary sieve analysis. This modeling framework may also inform the study of VRC01 in the treatment of HIV-infected persons.
The problem of nonparametric inference on a monotone function has been extensively studied in many particular cases. Estimators considered have often been of so-called Grenander type, being ...representable as the left derivative of the greatest convex minorant or least concave majorant of an estimator of a primitive function. In this paper, we provide general conditions for consistency and pointwise convergence in distribution of a class of generalized Grenander-type estimators of a monotone function. This broad class allows the minorization or majoratization operation to be performed on a data-dependent transformation of the domain, possibly yielding benefits in practice. Additionally, we provide simpler conditions and more concrete distributional theory in the important case that the primitive estimator and datadependent transformation function are asymptotically linear. We use our general results in the context of various well-studied problems, and show that we readily recover classical results established separately in each case. More importantly, we show that our results allow us to tackle more challenging problems involving parameters for which the use of flexible learning strategies appears necessary. In particular, we study inference on monotone density and hazard functions using informatively right-censored data, extending the classical work on independent censoring, and on a covariate-marginalized conditional mean function, extending the classical work on monotone regression functions.
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