Search

Objective To explore the application value of automatic nucleic acid extractor combined with vacuum concentrator in forensic DNA extraction. Methods Gradient samples of human peripheral venous blood were collected at 40, 80, 120, 160, 200, 240, 280 and 320 fold dilution. The samples of each gradient were treated with no inhibitor, black oil, rust, fruit acid, tin foil and indigo, respectively. The automatic nucleic acid extractor was used for DNA purification and extraction of the above samples. The extracted DNA eluent （6 μL） was taken for amplification directly, and the rest was concentrated by vacuum concentrator. DNA was amplified and examined using the Investigator 26plex QS kit before and after concentration. Results Only gradient samples treated with fruit acid obtained complete STR typing results at 40 fold dilution. The other 5 methods obtained complete STR typing results at 40-160 fold dilution. The results of STR typing after DNA concentration showed that the average peak height and detection rates of gene loci both increased to a certain extent, but the effect was not obvious. Conclusion The automatic nucleic acid extractor has an efficient inhibitor removal ability and high extracting efficiency of DNA. The vacuum concentrator can concentrate DNA samples to a certain extent. Combining the automatic nucleic acid extractor with the vacuum concentrator can improve the examination success rate of forensic materials.

This document analyzes the variation in excess mortality rates across different states in the United States during the COVID-19 pandemic. It estimates the number of excess deaths and the potential number of deaths that could have been averted if all states had matched the lowest excess death rates. The findings suggest that a significant number of excess deaths could have potentially been prevented if all states had matched the rates of the ten states with the lowest excess mortality. The study also examines the correlation between excess mortality and vaccination rates, finding that the inverse correlation decreased over time. The document emphasizes the importance of reducing health disparities, increasing vaccination rates, and implementing effective measures during future outbreaks. [Extracted from the article]

Background While prior work examining severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern focused on hospitalization and death, less is known about differences in clinical presentation. We compared the prevalence of acute symptoms across pre-Delta, Delta, and Omicron. Methods We conducted an analysis of the Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE), a cohort study enrolling symptomatic SARS-CoV-2-positive participants. We determined the association between the pre-Delta, Delta, and Omicron time periods and the prevalence of 21 coronavirus disease 2019 (COVID-19) acute symptoms. Results We enrolled 4113 participants from December 2020 to June 2022. Pre-Delta vs Delta vs Omicron participants had increasing sore throat (40.9%, 54.6%, 70.6%; P <.001), cough (50.9%, 63.3%, 66.7%; P <.001), and runny noses (48.9%, 71.3%, 72.9%; P <.001). We observed reductions during Omicron in chest pain (31.1%, 24.2%, 20.9%; P <.001), shortness of breath (42.7%, 29.5%, 27.5%; P <.001), loss of taste (47.1%, 61.8%, 19.2%; P <.001), and loss of smell (47.5%, 55.6%, 20.0%; P <.001). After adjustment, those infected during Omicron had significantly higher odds of sore throat vs pre-Delta (odds ratio [OR], 2.76; 95% CI, 2.26–3.35) and Delta (OR, 1.96; 95% CI, 1.69–2.28). Conclusions Participants infected during Omicron were more likely to report symptoms of common respiratory viruses, such as sore throat, and less likely to report loss of smell and taste. Trial registration NCT04610515. [ABSTRACT FROM AUTHOR]

Background The prevalence, incidence, and interrelationships of persistent symptoms after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection vary. There are limited data on specific phenotypes of persistent symptoms. Using latent class analysis (LCA) modeling, we sought to identify whether specific phenotypes of COVID-19 were present 3 months and 6 months post-infection. Methods This was a multicenter study of symptomatic adults tested for SARS-CoV-2 with prospectively collected data on general symptoms and fatigue-related symptoms up to 6 months postdiagnosis. Using LCA, we identified symptomatically homogenous groups among COVID-positive and COVID-negative participants at each time period for both general and fatigue-related symptoms. Results Among 5963 baseline participants (4504 COVID-positive and 1459 COVID-negative), 4056 had 3-month and 2856 had 6-month data at the time of analysis. We identified 4 distinct phenotypes of post-COVID conditions (PCCs) at 3 and 6 months for both general and fatigue-related symptoms; minimal-symptom groups represented 70% of participants at 3 and 6 months. When compared with the COVID-negative cohort, COVID-positive participants had higher occurrence of loss of taste/smell and cognition problems. There was substantial class-switching over time; those in 1 symptom class at 3 months were equally likely to remain or enter a new phenotype at 6 months. Conclusions We identified distinct classes of PCC phenotypes for general and fatigue-related symptoms. Most participants had minimal or no symptoms at 3 and 6 months of follow-up. Significant proportions of participants changed symptom groups over time, suggesting that symptoms present during the acute illness may differ from prolonged symptoms and that PCCs may have a more dynamic nature than previously recognized. Clinical Trials Registration.  NCT04610515. [ABSTRACT FROM AUTHOR]

Background: The delivery and financing of health care services were altered in unprecedented ways by COVID‐19 and subsequent policy responses. We estimated reimbursement losses to emergency physicians in 2020 compared to 2019 related to shifting acute care utilization during COVID‐19. Methods: This was an observational analysis of the Clinical Emergency Department Registry (CEDR) and the Nationwide Emergency Department Sample (NEDS). Study sample included all ED visits from a sample of 214 emergency department (ED) sites in the CEDR in 2019 and 2020 as well as all ED visits in the NEDS in 2019. We identified level of service billing code for evaluation and management (E&M) services, insurance payer, and geographic location of ED visits across sites in the CEDR and linked these to fee schedules to estimate total professional reimbursement across sites. Our primary analysis was to estimate reimbursement in 2020 compared to 2019 across the CEDR sites. In our secondary analysis, we linked sites in the CEDR to those in NEDS to estimate nationwide reimbursement. Results: Total E&M reimbursement for emergency physicians in the CEDR was $1.6 billion in 2019 and $1.3 billion in 2020, reflecting a 19.7% decline year over year ($308 million loss). In our secondary analysis, we estimate nationwide losses of $6.6 billion, a −19.4% decline year over year. If emergency physicians had received maximum allowable federal relief funds via CARES Act Phases 1 to 3 (2% of 2019 revenue) this would sum to $680 million (2% of the $34 billion) or 10.3% of the estimated $6.6 billion pandemic‐related losses. Conclusions: Our analyses provide an estimate of the scale of economic impacts of the COVID‐19 pandemic. These findings warrant consideration for policymaker relief and future redesign of emergency care financing. Ultimately, the COVID‐19 pandemic likely expanded known cracks in the financing of health care into steep fault lines. [ABSTRACT FROM AUTHOR]

Background Long-term symptoms following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are a major concern, yet their prevalence is poorly understood. Methods We conducted a prospective cohort study comparing adults with SARS-CoV-2 infection (coronavirus disease-positive [COVID+]) with adults who tested negative (COVID−), enrolled within 28 days of a Food and Drug Administration (FDA)-approved SARS-CoV-2 test result for active symptoms. Sociodemographic characteristics, symptoms of SARS-CoV-2 infection (assessed with the Centers for Disease Control and Prevention [CDC] Person Under Investigation Symptom List), and symptoms of post-infectious syndromes (ie, fatigue, sleep quality, muscle/joint pains, unrefreshing sleep, and dizziness/fainting, assessed with CDC Short Symptom Screener for myalgic encephalomyelitis/chronic fatigue syndrome) were assessed at baseline and 3 months via electronic surveys sent via text or email. Results Among the first 1000 participants, 722 were COVID+ and 278 were COVID−. Mean age was 41.5 (SD 15.2); 66.3% were female, 13.4% were Black, and 15.3% were Hispanic. At baseline, SARS-CoV-2 symptoms were more common in the COVID+ group than the COVID− group. At 3 months, SARS-CoV-2 symptoms declined in both groups, although were more prevalent in the COVID+ group: upper respiratory symptoms/head/eyes/ears/nose/throat (HEENT; 37.3% vs 20.9%), constitutional (28.8% vs 19.4%), musculoskeletal (19.5% vs 14.7%), pulmonary (17.6% vs 12.2%), cardiovascular (10.0% vs 7.2%), and gastrointestinal (8.7% vs 8.3%); only 50.2% and 73.3% reported no symptoms at all. Symptoms of post-infectious syndromes were similarly prevalent among the COVID+ and COVID− groups at 3 months. Conclusions Approximately half of COVID+ participants, as compared with one-quarter of COVID− participants, had at least 1 SARS-CoV-2 symptom at 3 months, highlighting the need for future work to distinguish long COVID. Clinical Trials Registration NCT04610515. [ABSTRACT FROM AUTHOR]

Background The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI. Methods and findings We used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749-0.754) versus 0.711 (95% CI 0.708-0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615-0.0618) versus 0.0636 (95% CI 0.0634-0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988-1.028) versus 1.036 (95% CI 1.015-1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015-March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors. Conclusions Machine learning techniques and data-driven approaches resulted in improved prediction of AKI risk after PCI. The results support the potential of these techniques for improving risk prediction models and identification of patients who may benefit from risk-mitigation strategies., Author(s): Chenxi Huang 1, Karthik Murugiah 2, Shiwani Mahajan 1, Shu-Xia Li 1, Sanket S. Dhruva 3,4, Julian S. Haimovich 5, Yongfei Wang 1, Wade L. Schulz 1,6, Jeffrey M. [...]

The aim of the present study was to detect the association between platelet glycoprotein-specific autoantibodies and the patient response to short-term high-dose dexamethasone (HD-DXM) + prednisone maintenance treatment. The data from 112 adult patients newly diagnosed with ITP who were administered first-line HD-DXM + prednisone maintenance therapy between January 2016 and January 2021 were retrospectively analyzed. A total of 72 patients positive for platelet glycoprotein-specific antibodies were enrolled in the antibody-positive group, and 40 patients not positive for platelet glycoprotein-specific antibodies were enrolled in the antibody-negative group. In the antibody-positive group, six platelet glycoprotein-specific antibody types were found: 41.67% of the patients were anti-GP IIb/IIIa-positive only, 5.56% were anti-GP Ib/IX-positive only, 5.56% were anti-P-selectin-positive only, 19.44% were anti-GP IIb/IIIa- and anti-GP Ib/IX-positive, 16.67% were anti-GP Ib/IX- and P-selectin-positive and 11.11% were positive for all three antibodies. There was no significant difference in the overall response rate between the antibody-positive group and the antibody-negative group (94.44 versus 80.00%, p =.221). However, the CR rate was significantly higher in the antibody-positive group than in the antibody-negative group (69.44% versus 40.00%, p =.032). The logistic regression analysis revealed that platelet glycoprotein-specific antibody positivity and age were two factors that could affect patient response. The present study discovered that adult patients newly diagnosed with ITP who had positive platelet glycoprotein-specific antibody test results were likely to achieve a better response after treatment with HD-DXM + prednisone maintenance. [ABSTRACT FROM AUTHOR]

Previous investigations have shown that [approximately equal to] 35% of the 90 tumors analyzed to date contain mutations within the DNA polymerase [beta] (pol [beta]) gene. The existence of pol [beta] mutations in a substantial fraction of human tumors studied suggests a link between DNA pol [beta] and cancer. A DNA pol [beta] variant, in which Lys-289 has been altered to Met, was identified previously in a colorectal carcinoma. The K289M protein was expressed in mouse L cells containing the [lambda] cll mutational target. The [lambda] DNA was packaged and used to infect bacterial cells to obtain the spontaneous mutation frequency. We found that expression of K289M in the mouse cells resulted in a 2.5-fold increase in the mutation frequency. What was most interesting was that expression of K289M in these cells resulted in a 16-fold increase in the frequency of C to G or G to C base substitutions at a specific site within the cll target. By using this cll target sequence, kinetic analysis of the purified K289M protein revealed that it was able to misincorporate dCTP opposite template C and dGTP opposite template G with significantly higher efficiency than the wild-type pol [beta] protein. We provide evidence that misincorporation of nucleotides by K289M results from altered positioning of the DNA within the active site of the enzyme. Our data are consistent with the interpretation that misincorporation of nucleotides resulting from altered DNA positioning by the K289M protein has the potential to result in tumorigenesis or neoplastic progression.

Kinetic and structural studies demonstrate that the M282L mutator mutant of DNA polymerase beta exhibits a decreased polymerase fidelity and the hydrophobic core around amino acid residue 282 imparts protein stability of the mutator mutant. Data indicate that M282L has an 11-fold decrease in dNTP substrate discrimination.

The F272L mutator mutant of polymerase beta was characterized. To determine the function of the F272 residue in maintaining polymerase beta fidelity, the in vivo and in vitro spontaneous mutation frequencies of the F272L variant were measured and a mutation spectrum was generated. Results indicated that F272L has a spontaneous mutation frequency that is 10-times greater than wild-type DNA polymerase beta and that phenylalanine 272 is essential for fidelity during ground-state binding.

Background: New methods such as machine learning techniques have been increasingly used to enhance the performance of risk predictions for clinical decision-making. However, commonly reported performance metrics may not be sufficient to capture the advantages of these newly proposed models for their adoption by health care professionals to improve care. Machine learning models often improve risk estimation for certain subpopulations that may be missed by these metrics.Methods and Results: This article addresses the limitations of commonly reported metrics for performance comparison and proposes additional metrics. Our discussions cover metrics related to overall performance, discrimination, calibration, resolution, reclassification, and model implementation. Models for predicting acute kidney injury after percutaneous coronary intervention are used to illustrate the use of these metrics.Conclusions: We demonstrate that commonly reported metrics may not have sufficient sensitivity to identify improvement of machine learning models and propose the use of a comprehensive list of performance metrics for reporting and comparing clinical risk prediction models. [ABSTRACT FROM AUTHOR]

DNA polymerase [Beta] (pol [Beta]) is a 39-kDa protein that functions in DNA repair processes in mammalian cells. As a first step toward understanding mechanisms of polymerase fidelity, we developed a genetic method to identify mammalian pol [Beta] mutator mutants. This screen takes advantage of a microbial genetics assay and the ability of rat pol [Beta] to substitute for Escherichia coli DNA polymerase I in DNA replication in vivo. Using this screen, we identified 13 candidate pol [Beta] mutator routants. Three of the candidate mutator routants were further characterized in vivo and shown to confer an increased spontaneous mutation frequency over that of wild-type pol [Beta] to our bacterial strain. Purification and subsequent analysis of one of our putative mutator proteins, the pol [Beta]-14 protein, showed that it possesses intrinsic mutator activity in four different assays that measure the fidelity of DNA synthesis. Therefore, residue 265, which is altered in pol [Beta]-14 and another of our mutant proteins, pol [Beta]-166, is probably critical for accurate DNA synthesis by pol [Beta]. Thus, our genetic method of screening for pol [Beta] mutator routants is useful in identifying active mammalian DNA polymerase mutants that encode enzymes that catalyze DNA synthesis with altered fidelity compared with the wild-type pol [Beta] enzyme.

The COVID-19 pandemic has disproportionately affected Hispanic or Latino, non-Hispanic Black (Black), non-Hispanic American Indian or Alaska Native (AI/AN), and non-Hispanic Native Hawaiian or Other Pacific Islander (NH/PI) populations in the United States. These populations have experienced higher rates of infection and mortality compared with the non-Hispanic White (White) population (1-5) and greater excess mortality (i.e., the percentage increase in the number of persons who have died relative to the expected number of deaths for a given place and time) (6). A limitation of existing research on excess mortality among racial/ethnic minority groups has been the lack of adjustment for age and population change over time. This study assessed excess mortality incidence rates (IRs) (e.g., the number of excess deaths per 100,000 person-years) in the United States during December 29, 2019-January 2, 2021, by race/ethnicity and age group using data from the National Vital Statistics System. Among all assessed racial/ethnic groups (non-Hispanic Asian [Asian], AI/AN, Black, Hispanic, NH/PI, and White populations), excess mortality IRs were higher among persons aged ≥65 years (426.4 to 1033.5 excess deaths per 100,000 person-years) than among those aged 25-64 years (30.2 to 221.1) and those aged <25 years (-2.9 to 14.1). Among persons aged <65 years, Black and AI/AN populations had the highest excess mortality IRs. Among adults aged ≥65 years, Black and Hispanic persons experienced the highest excess mortality IRs of >1,000 excess deaths per 100,000 person-years. These findings could help guide more tailored public health messaging and mitigation efforts to reduce disparities in mortality associated with the COVID-19 pandemic in the United States,* by identifying the racial/ethnic groups and age groups with the highest excess mortality rates. [ABSTRACT FROM AUTHOR]

Although timely administration of antibiotics has an established benefit in serious bacterial infection, the majority of studies evaluating antibiotic delay focus only on the first dose. Recent evidence suggests that delays in redosing may also be associated with worse clinical outcome. In light of the increasing burden of boarding in Emergency Departments (ED) and subsequent need to redose antibiotic in the ED, we examined the association between delayed second antibiotic dose administration and mortality among patients admitted from the ED with a broad array of infections and characterized risk factors associated with delayed second dose administration. We performed a retrospective cohort study of patients admitted through five EDs in a single healthcare system from 1/2018 through 12/2018. Our study included all patients, aged 18 years or older, who received two intravenous antibiotic doses within a 30-h period, with the first dose administered in the ED. Patients with end stage renal disease, cirrhosis and extremes of weight were excluded due to a lack of consensus on antibiotic dosing intervals for these populations. Delay was defined as administration of the second dose at a time-point greater than 125% of the recommended interval. The primary outcome was in-hospital mortality. A total of 5605 second antibiotic doses, occurring during 4904 visits, met study criteria. Delayed administration of the second dose occurred during 21.1% of visits. After adjustment for patient characteristics, delayed second dose administration was associated with increased odds of in-hospital mortality (OR 1.50, 95%CI 1.05–2.13). Regarding risk factors for delay, every one-hour increase in allowable compliance time was associated with a 18% decrease in odds of delay (OR 0.82 95%CI 0.75–0.88). Other risk factors for delay included ED boarding more than 4 h (OR 1.47, 95%CI 1.27–1.71) or a high acuity presentation as defined by emergency severity index (ESI) (OR 1.54, 95%CI 1.30–1.81 for ESI 1–2 versus 3–5). Delays in second antibiotic dose administration were frequent in the ED and early hospital course, and were associated with increased odds of in-hospital mortality. Several risk factors associated with delays in second dose administration, including ED boarding, were identified. [ABSTRACT FROM AUTHOR]

This study uses national death certificate data to characterize trends in death and excess mortality from drug overdoses, homicides, unintentional injuries, motor vehicle crashes, and suicide during the first 6 months of the pandemic in the US. [ABSTRACT FROM AUTHOR]

In order to improve convergence rate and optimization precision of the cuckoo search (CS) algorithm, an improved dynamic self-adaption cuckoo search algorithm based on collaboration between subpopulations (SC-DSCS, where 'SC' represents 'Subpopulation Collaboration,' 'DS' represents 'dynamic self-adaption') is proposed. In SC-DSCS, the population of cuckoos is divided into two subgroups. The nest locations of birds belonging to the first subgroup are updated according to the traditional CS algorithm so as to retain the global search ability, and the second subgroup produces the corresponding nest locations for next generation by flying from the better nest locations to enhance the local search ability of the CS algorithm. Through collaboration between two subgroups, the problem that the local search ability of CS algorithm is not strong can be effectively solved. In order to reduce the probability of the algorithm falling into local optimum and improve the optimization precision, the SC-DSCS algorithm creates a new bird's nest under the comprehensive assessment of the first three best bird's nests. The new nest is added to the optimal bird's nest sequence. In order to improve the adaptability of the SC-DSCS, adaptive step length control is adopted in the bird's nest position updating process. Finally, nine benchmark functions are adopted to carry out the simulation experiments. The proposed algorithm is compared with particle swarm optimization algorithm, artificial colony algorithm and differential evolution algorithm. Simulation results show that the proposed SC-DSCS algorithm has better convergence speed and optimization precision. [ABSTRACT FROM AUTHOR]

Purpose: To estimate medical device utilization needed to detect safety differences among implantable cardioverter defibrillators (ICDs) generator models and compare these estimates to utilization in practice. Methods: We conducted repeated sample size estimates to calculate the medical device utilization needed, systematically varying device-specific safety event rate ratios and significance levels while maintaining 80% power, testing 3 average adverse event rates (3.9, 6.1, and 12.6 events per 100 person-years) estimated from the American College of Cardiology's 2006 to 2010 National Cardiovascular Data Registry of ICDs. We then compared with actual medical device utilization. Results: At significance level 0.05 and 80% power, 34% or fewer ICD models accrued sufficient utilization in practice to detect safety differences for rate ratios <1.15 and an average event rate of 12.6 events per 100 person-years. For average event rates of 3.9 and 12.6 events per 100 person-years, 30% and 50% of ICD models, respectively, accrued sufficient utilization for a rate ratio of 1.25, whereas 52% and 67% for a rate ratio of 1.50. Because actual ICD utilization was not uniformly distributed across ICD models, the proportion of individuals receiving any ICD that accrued sufficient utilization in practice was 0% to 21%, 32% to 70%, and 67% to 84% for rate ratios of 1.05, 1.15, and 1.25, respectively, for the range of 3 average adverse event rates. Conclusions: Small safety differences among ICD generator models are unlikely to be detected through routine surveillance given current ICD utilization in practice, but large safety differences can be detected for most patients at anticipated average adverse event rates. [ABSTRACT FROM AUTHOR]

Randomized trials of hypertension have seldom examined heterogeneity in response to treatments over time and the implications for cardiovascular outcomes. Understanding this heterogeneity, however, is a necessary step toward personalizing antihypertensive therapy. We applied trajectory-based modeling to data on 39 763 study participants of the ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial) to identify distinct patterns of systolic blood pressure (SBP) response to randomized medications during the first 6 months of the trial. Two trajectory patterns were identified: immediate responders (85.5%), on average, had a decreasing SBP, whereas nonimmediate responders (14.5%), on average, had an initially increasing SBP followed by a decrease. Compared with those randomized to chlorthalidone, participants randomized to amlodipine (odds ratio, 1.20; 95% confidence interval [CI], 1.10-1.31), lisinopril (odds ratio, 1.88; 95% CI, 1.73-2.03), and doxazosin (odds ratio, 1.65; 95% CI, 1.52-1.78) had higher adjusted odds ratios associated with being a nonimmediate responder (versus immediate responder). After multivariable adjustment, nonimmediate responders had a higher hazard ratio of stroke (hazard ratio, 1.49; 95% CI, 1.21-1.84), combined cardiovascular disease (hazard ratio, 1.21; 95% CI, 1.11-1.31), and heart failure (hazard ratio, 1.48; 95% CI, 1.24-1.78) during follow-up between 6 months and 2 years. The SBP response trajectories provided superior discrimination for predicting downstream adverse cardiovascular events than classification based on difference in SBP between the first 2 measurements, SBP at 6 months, and average SBP during the first 6 months. Our findings demonstrate heterogeneity in response to antihypertensive therapies and show that chlorthalidone is associated with more favorable initial response than the other medications. [ABSTRACT FROM AUTHOR]

Identifying temporal variation in hospitalization rates may provide insights about disease patterns and thereby inform research, policy, and clinical care. However, the majority of medical conditions have not been studied for their potential seasonal variation. The objective of this study was to apply a data-driven approach to characterize temporal variation in condition-specific hospitalizations. Using a dataset of 34 million inpatient discharges gathered from hospitals in New York State from 2008–2011, we grouped all discharges into 263 clinical conditions based on the principal discharge diagnosis using Clinical Classification Software in order to mitigate the limitation that administrative claims data reflect clinical conditions to varying specificity. After applying Seasonal-Trend Decomposition by LOESS, we estimated the periodicity of the seasonal component using spectral analysis and applied harmonic regression to calculate the amplitude and phase of the condition’s seasonal utilization pattern. We also introduced four new indices of temporal variation: mean oscillation width, seasonal coefficient, trend coefficient, and linearity of the trend. Finally, K-means clustering was used to group conditions across these four indices to identify common temporal variation patterns. Of all 263 clinical conditions considered, 164 demonstrated statistically significant seasonality. Notably, we identified conditions for which seasonal variation has not been previously described such as ovarian cancer, tuberculosis, and schizophrenia. Clustering analysis yielded three distinct groups of conditions based on multiple measures of seasonal variation. Our study was limited to New York State and results may not directly apply to other regions with distinct climates and health burden. A substantial proportion of medical conditions, larger than previously described, exhibit seasonal variation in hospital utilization. Moreover, the application of clustering tools yields groups of clinically heterogeneous conditions with similar seasonal phenotypes. Further investigation is necessary to uncover common etiologies underlying these shared seasonal phenotypes. [ABSTRACT FROM AUTHOR]