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Background Timely initiation of physical, occupational, and speech therapy in critically ill patients is crucial to reduce morbidity and improve outcomes. Over a 5-year time interval, we sought to determine the utilization of these rehabilitation therapies in the USA. Methods We performed a retrospective cohort study utilizing a large, national administrative database including ICU patients from 591 hospitals. Patients over 18 years of age with acute respiratory failure requiring invasive mechanical ventilation within the first 2 days of hospitalization and for a duration of at least 48 h were included. Results A total of 264,137 patients received invasive mechanical ventilation for a median of 4.0 [2.0–8.0] days. Overall, patients spent a median of 5.0 [3.0–10.0] days in the ICU and 10.0 [7.0–16.0] days in the hospital. During their hospitalization, 66.5%, 41.0%, and 33.2% (95% CI = 66.3–66.7%, 40.8–41.2%, 33.0–33.4%, respectively) received physical, occupational, and speech therapy. While on mechanical ventilation, 36.2%, 29.7%, and 29.9% (95% CI = 36.0–36.4%, 29.5–29.9%, 29.7–30.1%) received physical, occupational, and speech therapy. In patients receiving therapy, their first physical therapy session occurred on hospital day 5 [3.0–8.0] and hospital day 6 [4.0–10.0] for occupational and speech therapy. Of all patients, 28.6% (95% CI = 28.4–28.8%) did not receive physical, occupational, or speech therapy during their hospitalization. In a multivariate analysis, patients cared for in the Midwest and at teaching hospitals were more likely to receive physical, occupational, and speech therapy (all P  < 0.05). Of patients with identical covariates receiving therapy, there was a median of 61%, 187%, and 70% greater odds of receiving physical, occupational, and speech therapy, respectively, at one randomly selected hospital compared with another (median odds ratio 1.61, 2.87, 1.70, respectively). Conclusions Physical, occupational, and speech therapy are not routinely delivered to critically ill patients, particularly while on mechanical ventilation in the USA. The utilization of these therapies varies according to insurance coverage, geography, and hospital teaching status, and at a hospital level.

Medicare insurance claims may provide an efficient means to ascertain follow-up of older participants in clinical research. We sought to determine the accuracy and completeness of claims- versus site-based follow-up with clinical event committee (+CEC) adjudication of cardiovascular outcomes. We performed a retrospective study using linked Medicare and Duke Database of Clinical Trials data. Medicare claims were linked to clinical data from 7 randomized cardiovascular clinical trials. Of 52,476 trial participants, linking resulted in 5,839 (of 10,497 linkage-eligible) Medicare-linked trial participants with fee-for-service A and B coverage. Death, myocardial infarction (MI), stroke, and revascularization incidences were compared using Medicare inpatient claims only, site-reported events (+CEC) only, or a combination of the 2. Randomized treatment effects were compared as a function of whether claims-based, site-based (+CEC), or a combined system was used for event detection. Among the 5,839 study participants, the annual event rates were similar between claims- and site-based (+CEC) follow-up: death (overall rate 5.2% vs 5.2%; adjusted κ 0.99), MI (2.2% vs 2.3%; adjusted κ 0.96), stroke (0.7% vs 0.7%; adjusted κ 0.99), and any revascularization (7.4% vs 7.9%; adjusted κ 0.95). Of events detected by claims yet not reported by CEC, a minority were reported by sites but negatively adjudicated by CEC (39% of MIs and 18% of strokes). Differences in individual case concordance led to higher event rates when claims- and site-based (+CEC) systems were combined. Randomized treatment effects were similar among the 3 approaches for each outcome of interest. Claims- versus site-based (+CEC) follow-up identified similar overall cardiovascular event rates despite meaningful differences in the events detected. Randomized treatment effects were similar using the 2 methods, suggesting claims data could be used to support clinical research leveraging routinely collected data. This approach may lead to more effective evidence generation, synthesis, and appraisal of medical products and inform the strategic approaches toward the National Evaluation System for Health Technology.

Background Validated algorithms to classify type 1 and 2 diabetes (T1D, T2D) are mostly limited to white pediatric populations. We conducted a large study in Hong Kong among children and adults with diabetes to develop and validate algorithms using electronic health records (EHRs) to classify diabetes type against clinical assessment as the reference standard, and to evaluate performance by age at diagnosis. Methods We included all people with diabetes (age at diagnosis 1.5–100 years during 2002–15) in the Hong Kong Diabetes Register and randomized them to derivation and validation cohorts. We developed candidate algorithms to identify diabetes types using encounter codes, prescriptions, and combinations of these criteria (“combination algorithms”). We identified 3 algorithms with the highest sensitivity, positive predictive value (PPV), and kappa coefficient, and evaluated performance by age at diagnosis in the validation cohort. Results There were 10,196 (T1D n  = 60, T2D n  = 10,136) and 5101 (T1D n  = 43, T2D n  = 5058) people in the derivation and validation cohorts (mean age at diagnosis 22.7, 55.9 years; 53.3, 43.9% female; for T1D and T2D respectively). Algorithms using codes or prescriptions classified T1D well for age at diagnosis < 20 years, but sensitivity and PPV dropped for older ages at diagnosis. Combination algorithms maximized sensitivity or PPV, but not both. The “high sensitivity for type 1” algorithm (ratio of type 1 to type 2 codes ≥ 4, or at least 1 insulin prescription within 90 days) had a sensitivity of 95.3% (95% confidence interval 84.2–99.4%; PPV 12.8%, 9.3–16.9%), while the “high PPV for type 1” algorithm (ratio of type 1 to type 2 codes ≥ 4, and multiple daily injections with no other glucose-lowering medication prescription) had a PPV of 100.0% (79.4–100.0%; sensitivity 37.2%, 23.0–53.3%), and the “optimized” algorithm (ratio of type 1 to type 2 codes ≥ 4, and at least 1 insulin prescription within 90 days) had a sensitivity of 65.1% (49.1–79.0%) and PPV of 75.7% (58.8–88.2%) across all ages. Accuracy of T2D classification was high for all algorithms. Conclusions Our validated set of algorithms accurately classifies T1D and T2D using EHRs for Hong Kong residents enrolled in a diabetes register. The choice of algorithm should be tailored to the unique requirements of each study question.

IntroductionTwo randomized controlled trials (RCTs) from Europe recently showed similar weight loss and rates of type 2 diabetes (T2D) remission following laparoscopic gastric bypass (LRYGB) and laparoscopic sleeve gastrectomy (LSG). However, results from observational studies in the United States (US) have discordant results. We compared 1-year weight loss and T2D remission between LRYGB and LSG in a heterogeneous patient cohort from the US, albeit with similar inclusion and exclusion criteria to the European RCTs.MethodsLogistic regression was used to propensity match LSG and LRYGB patients according to age, gender, race, preoperative BMI, and T2D. Inclusion and exclusion criteria were adopted from the two European RCTs. Demographic, anthropometric, weight outcomes, and comorbidities prevalence were compared at baseline and 1-year follow-up.ResultsWe included 278 patients (139 LSG and 139 RYGB; median age 42 years, 89% female, 57% black race, 22% with public health insurance, and 25% with T2D). One year after surgery, mean %EWL was 77.3 ± 19.5% with LRYGB and 63.1 ± 21% with LSG (P < 0.001). Mean %TWL was 34.2 ± 7.3% after LRYGB and 28.1 ± 8.2% after LSG, (P < 0.001). The proportion of patients who achieved T2D remission was comparable between surgeries (LRGYB: 68.6% vs. LSG: 66.7%, P = 0.89). LSG, older age, black race, and higher preoperative BMI were independently associated with lower %EWL. Independent correlates of weight loss were different for LRYGB and LSG.ConclusionsWeight loss, but not the likelihood of T2D remission, was greater with LRYGB than LSG in a diverse patient cohort in the US. Further research efforts connecting population diversity to discordant results across studies is needed to better counsel patients with regards to expected postoperative outcomes.

Objectives The Hospital Episode Statistics (HES) dataset is a source of administrative ‘big data’ with potential for costing purposes in economic evaluations alongside clinical trials. This study assesses the validity of coverage in the HES outpatient dataset. Methods Men who died of, or with, prostate cancer were selected from a prostate-cancer screening trial (CAP, Cluster randomised triAl of PSA testing for Prostate cancer). Details of visits that took place after 1/4/2003 to hospital outpatient departments for conditions related to prostate cancer were extracted from medical records (MR); these appointments were sought in the HES outpatient dataset based on date. The matching procedure was repeated for periods before and after 1/4/2008, when the HES outpatient dataset was accredited as a national statistic. Results 4922 outpatient appointments were extracted from MR for 370 men. 4088 appointments recorded in MR were identified in the HES outpatient dataset (83.1 %; 95 % confidence interval [CI] 82.0–84.1). For appointments occurring prior to 1/4/2008, 2195/2755 (79.7 %; 95 % CI 78.2–81.2) matches were observed, while 1893/2167 (87.4 %; 95 % CI 86.0–88.9) appointments occurring after 1/4/2008 were identified ( p for difference <0.001). 215/370 men (58.1 %) had at least one appointment in the MR review that was unmatched in HES, 155 men (41.9 %) had all their appointments identified, and 20 men (5.4 %) had no appointments identified in HES. Conclusions The HES outpatient dataset appears reasonably valid for research, particularly following accreditation. The dataset may be a suitable alternative to collecting MR data from hospital notes within a trial, although caution should be exercised with data collected prior to accreditation.

Objective: Formatting of adverse drug reaction (ADR) information differs among drug information (DI) resources and may impact clinical decision-making. The objective of this study was to determine whether ADR formatting impacts adverse event interpretation by pharmacy practitioners and students.Methods: Participants were randomized to receive ADR information in a comparative quantitative (CQUANT), noncomparative quantitative (NQUANT), or noncomparative qualitative (NQUAL) format to interpret 3 clinical vignettes. Vignettes involved patients presenting with adverse events that varied in the extent to which they were associated with a medication. The primary outcome was interpretation of the likelihood of medication-induced adverse events on a 10-point Likert scale. Lower scoring on likelihood (i.e., ADR deemed unlikely) reflected more appropriate interpretation. Linear regression was performed to analyze the effects of ADR information format on the primary outcome.Results: A total of 108 participants completed the study (39 students and 69 pharmacists). Overall, the CQUANT group had the lowest average likelihood compared to NQUAL (4.0 versus 5.4; p<0.01) and NQUANT (4.0 versus 4.9; p=0.016) groups. There was no difference between NQUAL and NQUANT groups (5.4 versus 4.9; p=0.14). In the final model, at least 2 years of postgraduate training (–1.1; 95% CI: –1.8 to –0.3; p<0.01) and CQUANT formatting (–1.3; 95% CI: –0.9 to –1.7; p<0.01) were associated with reduced likelihood.Conclusions: Formatting impacts pharmacists’ and pharmacy students’ interpretation of ADR information. CQUANT formatting and at least two years of postgraduate training improved interpretation of adverse events.

Knowledge about use of health care services (health care utilization) and expenditures among unauthorized immigrant populations is uncertain because of limitations in ascertaining legal status in population data. To examine health care utilization and expenditures that are attributable to unauthorized and authorized immigrants vs US-born individuals. This cross-sectional study used the data on documentation status from the Los Angeles Family and Neighborhood Survey (LAFANS) to develop a random forest classifier machine learning model. K-fold cross-validation was used to test model performance. The LAFANS is a randomized, multilevel, in-person survey of households residing in Los Angeles County, California, consisting of 2 waves. Wave 1 began in April 2000 and ended in January 2002, and wave 2 began in August 2006 and ended in December 2008. The machine learning model was then applied to a nationally representative database, the 2016-2017 Medical Expenditure Panel Survey (MEPS), to predict health care expenditures and utilization among unauthorized and authorized immigrants and US-born individuals. A generalized linear model analyzed health care expenditures. Logistic regression modeling estimated dichotomous use of emergency department (ED), inpatient, outpatient, and office-based physician visits by immigrant groups with adjusting for confounding factors. Data were analyzed from May 1, 2019, to October 14, 2020. Self-reported immigration status (US-born, authorized, and unauthorized status). Annual health care expenditures per capita and use of ED, outpatient, inpatient, and office-based physician care. Of 47 199 MEPS respondents with nonmissing data, 35 079 (74.3%) were US born, 10 816 (22.9%) were authorized immigrants, and 1304 (2.8%) were unauthorized immigrants (51.7% female; mean age, 47.6 [95% CI, 47.4-47.8] years). Compared with authorized immigrants and US-born individuals, unauthorized immigrants were more likely to be aged 18 to 44 years (80.8%), Latino (96.3%), and Spanish speaking (95.2%) and to have less than 12 years of education (53.7%). Half of unauthorized immigrants (47.1%) were uninsured compared with 15.9% of authorized immigrants and 6.0% of US-born individuals. Mean annual health care expenditures per person were $1629 (95% CI, $1330-$1928) for unauthorized immigrants, $3795 (95% CI, $3555-$4035) for authorized immigrants, and $6088 (95% CI, $5935-$6242) for US-born individuals. Contrary to much political discourse in the US, this cross-sectional study found no evidence that unauthorized immigrants are a substantial economic burden on safety net facilities such as EDs. This study illustrates the value of machine learning in the study of unauthorized immigrants using large-scale, secondary databases.

BackgroundAllopurinol reduces oxidative stress and may thus have an anti-inflammatory effect. Previous studies suggest that allopurinol use might decrease the risk of prostate cancer (PCa) among gout patients. We studied the association between allopurinol use and PCa incidence.MethodsThe cohort consists of 76,874 men without prevalent PCa, originally identified for the Finnish Randomized Study of Screening for Prostate Cancer (FinRSPC). The follow-up started at entry to the trial. We excluded men using allopurinol in the year before entry (wash-out). PCa cases detected during 1996–2015 were identified from the Finnish Cancer Registry. Information on tumor Gleason score and TNM stage were obtained from medical files. Information on PSA level was obtained from screening samples for men in the FinRSPC screening arm and from laboratory databases for men in the control arm. Information on BMI was based on a questionnaire sent to men in the FinRSPC screening arm in 2004–2008. Drug purchase information were obtained from the national prescription database. We used Cox regression (adjusted for age, FinRSPC trial arm, PCa family history and use of other medication) to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) of PCa risk by allopurinol use. We analyzed medication as a time-dependent variable to minimize immortal time bias.ResultsThere were 9062 new PCa diagnoses in the cohort. Follow-up time did not differ by allopurinol use (median 17 yr; IQR 11–19 vs median 17 yr; IQR 12.33–19). The risk of PCa did not differ by allopurinol use (multivariable adjusted HR 1.03; 95% CI 0.92–1.16). Allopurinol use did not associate with the risk of high-grade or metastatic cancer. Cumulative duration or average yearly dose of allopurinol use showed no association with PCa risk. No delayed risk associations were observed in the lag-time analyses.ConclusionsWe observed no difference in the PCa risk by allopurinol use.

Background Large observational datasets such as Clinical Practice Research Datalink (CPRD) provide opportunities to conduct clinical studies and economic evaluations with efficient designs. Objectives Our objectives were to report the economic evaluation methodology for a cluster randomised controlled trial (RCT) of a UK NHS-delivered public health intervention for children with asthma that was evaluated using CPRD and describe the impact of this methodology on results. Methods CPRD identified eligible patients using predefined asthma diagnostic codes and captured 1-year pre- and post-intervention healthcare contacts (August 2012 to July 2014). Quality-adjusted life-years (QALYs) 4 months post-intervention were estimated by assigning utility values to exacerbation-related contacts; a systematic review identified these utility values because preference-based outcome measures were not collected. Bootstrapped costs were evaluated 12 months post-intervention, both with 1-year regression-based baseline adjustment (BA) and without BA (observed). Results Of 12,179 patients recruited, 8190 (intervention 3641; control 4549) were evaluated in the primary analysis, which included patients who received the protocol-defined intervention and for whom CPRD data were available. The intervention’s per-patient incremental QALY loss was 0.00017 (bias-corrected and accelerated 95% confidence intervals [BCa 95% CI] –0.00051 to 0.00018) and cost savings were £14.74 (observed; BCa 95% CI –75.86 to 45.19) or £36.07 (BA; BCa 95% CI –77.11 to 9.67), respectively. The probability of cost savings was much higher when accounting for BA versus observed costs due to baseline cost differences between trial arms (96.3 vs. 67.3%, respectively). Conclusion Economic evaluations using data from a large observational database without any primary data collection is feasible, informative and potentially efficient. Clinical Trials Registration Number: ISRCTN03000938.

Rare diseases, an emerging global public health priority, require an evidence-based estimate of the global point prevalence to inform public policy. We used the publicly available epidemiological data in the Orphanet database to calculate such a prevalence estimate. Overall, Orphanet contains information on 6172 unique rare diseases; 71.9% of which are genetic and 69.9% which are exclusively pediatric onset. Global point prevalence was calculated using rare disease prevalence data for predefined geographic regions from the ‘Orphanet Epidemiological file’ (http://www.orphadata.org/cgi-bin/epidemio.html). Of the 5304 diseases defined by point prevalence, 84.5% of those analysed have a point prevalence of <1/1 000 000. However 77.3–80.7% of the population burden of rare diseases is attributable to the 4.2% (n = 149) diseases in the most common prevalence range (1–5 per 10 000). Consequently national definitions of ‘Rare Diseases’ (ranging from prevalence of 5 to 80 per 100 000) represent a variable number of rare disease patients despite sharing the majority of rare disease in their scope. Our analysis yields a conservative, evidence-based estimate for the population prevalence of rare diseases of 3.5–5.9%, which equates to 263–446 million persons affected globally at any point in time. This figure is derived from data from 67.6% of the prevalent rare diseases; using the European definition of 5 per 10 000; and excluding rare cancers, infectious diseases, and poisonings. Future registry research and the implementation of rare disease codification in healthcare systems will further refine the estimates.