36 research outputs found
Association between hospital case volume and mortality in non-elderly pneumonia patients stratified by severity: a retrospective cohort study
Background: The characteristics and aetiology of pneumonia in the non-elderly population is distinct from that in the elderly population. While a few studies have reported an inverse association between hospital case volume and clinical outcome in elderly pneumonia patients, the evidence is lacking in a younger population. In addition, the relationship between volume and outcome may be different in severe pneumonia cases than in mild cases. In this context, we tested two hypotheses: 1) non-elderly pneumonia patients treated at hospitals with larger case volume have better clinical outcome compared with those treated at lower case volume hospitals; 2) the volume-outcome relationship differs by the severity of the pneumonia. Methods: We conducted the study using the Japanese Diagnosis Procedure Combination database. Patients aged 18–64 years discharged from the participating hospitals between July to December 2010 were included. The hospitals were categorized into four groups (very-low, low, medium, high) based on volume quartiles. The association between hospital case volume and in-hospital mortality was evaluated using multivariate logistic regression with generalized estimating equations adjusting for pneumonia severity, patient demographics and comorbidity score, and hospital academic status. We further analyzed the relationship by modified A-DROP pneumonia severity score calculated using the four severity indices: dehydration, low oxygen saturation, orientation disturbance, and decreased systolic blood pressure. Results: We identified 8,293 cases of pneumonia at 896 hospitals across Japan, with 273 in-hospital deaths (3.3%). In the overall population, no significant association between hospital volume and in-hospital mortality was observed. However, when stratified by pneumonia severity score, higher hospital volume was associated with lower in-hospital mortality at the intermediate severity level (modified A-DROP score = 2) (odds ratio (OR) of very low vs. high: 2.70; 95% confidence interval (CI): 1.12–6.55, OR of low vs. high: 2.40; 95% CI:0.99–5.83). No significant association was observed for other severity strata. Conclusions: Hospital case volume was inversely associated with in-hospital mortality in non-elderly pneumonia patients with intermediate pneumonia severity. Our result suggests room for potential improvement in the quality of care in hospitals with lower volume, to improve treatment outcomes particularly in patients admitted with intermediate pneumonia severity
Dimension reduction and shrinkage methods for high dimensional disease risk scores in historical data
Abstract Background Multivariable confounder adjustment in comparative studies of newly marketed drugs can be limited by small numbers of exposed patients and even fewer outcomes. Disease risk scores (DRSs) developed in historical comparator drug users before the new drug entered the market may improve adjustment. However, in a high dimensional data setting, empirical selection of hundreds of potential confounders and modeling of DRS even in the historical cohort can lead to over-fitting and reduced predictive performance in the study cohort. We propose the use of combinations of dimension reduction and shrinkage methods to overcome this problem, and compared the performances of these modeling strategies for implementing high dimensional (hd) DRSs from historical data in two empirical study examples of newly marketed drugs versus comparator drugs after the new drugs’ market entry—dabigatran versus warfarin for the outcome of major hemorrhagic events and cyclooxygenase-2 inhibitor (coxibs) versus nonselective non-steroidal anti-inflammatory drugs (nsNSAIDs) for gastrointestinal bleeds. Results Historical hdDRSs that included predefined and empirical outcome predictors with dimension reduction (principal component analysis; PCA) and shrinkage (lasso and ridge regression) approaches had higher c-statistics (0.66 for the PCA model, 0.64 for the PCA + ridge and 0.65 for the PCA + lasso models in the warfarin users) than an unreduced model (c-statistic, 0.54) in the dabigatran example. The odds ratio (OR) from PCA + lasso hdDRS-stratification [OR, 0.64; 95 % confidence interval (CI) 0.46–0.90] was closer to the benchmark estimate (0.93) from a randomized trial than the model without empirical predictors (OR, 0.58; 95 % CI 0.41–0.81). In the coxibs example, c-statistics of the hdDRSs in the nsNSAID initiators were 0.66 for the PCA model, 0.67 for the PCA + ridge model, and 0.67 for the PCA + lasso model; these were higher than for the unreduced model (c-statistic, 0.45), and comparable to the demographics + risk score model (c-statistic, 0.67). Conclusions hdDRSs using historical data with dimension reduction and shrinkage was feasible, and improved confounding adjustment in two studies of newly marketed medications
Laparoscopic Surgery for Acute Diffuse Peritonitis Due to Gastrointestinal Perforation: A Nationwide Epidemiologic Study Using the National Clinical Database
[Background] Elective laparoscopic surgery is now widely accepted in the treatment of abdominal diseases because of its minimal invasiveness and rapid postoperative recovery. It is also used in the emergency setting for the diagnosis and treatment of acute diffuse peritonitis regardless of the causative disease. However, the value of laparoscopy in acute diffuse peritonitis remains unclear. In this study we aimed to show trends in the use of laparoscopy over time and compare the real-world performance of laparoscopic surgery with that of open surgery for acute diffuse peritonitis due to gastrointestinal perforation. [Methods] We extracted data from the National Clinical Database, a nationwide surgery registration system in Japan, for patients with a diagnosis of acute diffuse peritonitis due to gastroduodenal or colorectal perforation between 2016 and 2019. Trends in the use of laparoscopy over time were identified. Patient characteristics, laboratory findings, surgical findings, and postoperative complications were compared between laparoscopic surgery and open surgery. [Results] Patients in poor condition and those with abnormal laboratory findings tended to undergo open surgery. Anesthesia time and operating time were longer for laparoscopic surgery in patients with gastroduodenal perforation but shorter in those with colorectal perforation. Fewer complications occurred in patients who underwent laparoscopic surgery. The number of institutions where laparoscopic surgery was performed and the proportion of the use of laparoscopy at each institution increased over time. [Conclusion] The use of laparoscopy is becoming common in surgery for acute diffuse peritonitis due to gastrointestinal perforation. This approach may be a useful option for acute diffuse peritonitis
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Confounding Adjustment in Comparative Studies of Newly Marketed Medications
Observational studies of newly marketed medications can add important safety and effectiveness information to what is known from pre-approval randomized controlled trials. As new medications are often prescribed to select group of patients, confounding can be particularly large in this setting. Multivariable modeling of the exposure or the outcome is commonly used to reduce confounding, but the modeling can be challenged by rapidly evolving prescribing patterns and the limited number of patients exposed to the new medication. Furthermore, while adjustment for empirically identified potential confounders, and proxies thereof, has been shown to reduce confounding, it is difficult to empirically identify potential confounders in settings with small number of outcomes.
In this thesis, we explored two new methods to improve confounding control in the setting of newly marketed medications with few exposures and outcomes and many potential confounders. The first method is the high dimensional disease risk score (DRS) developed using an historical cohort of comparator drug initiators. We developed prediction models for the outcomes of interest in an historical cohort and applied the models to the concurrent study cohort of new and comparator drug initiators. We then used individual patient’s predicted risk score to balance the baseline risk between the two groups. In chapter 1, we compared combinations of shrinkage and dimension reduction approaches to reduce model over-fitting when developing DRSs from large numbers of potential covariates. In chapter 2, we compared the performance of the high dimensional DRSs to the standard high dimensional propensity score (hdPS) approach. In chapter 3, we developed a new method that augments the hdPS variable selection process with data from an historical cohort and compared this approach to standard hdPS. All evaluations were conducted in example comparative studies of newly marketed medications using large US claims database.
The hdPS with variable selection augmented by historical data showed good performance in confounding adjustment even in small outcome settings. Future studies should evaluate the use of this method in other settings and should explore improvements in the use of high dimensional DRSs