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  • 1
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    Difference in 30-Day Readmission Rates After Laparoscopic Sleeve Gastrectomy Versus Laparoscopic Roux-En-Y Gastric Bypass: a Propensity Score Matched Study Using ACS NSQIP Data (2015–2019)
    Springer Science and Business Media LLC ; 2023
    In:  Obesity Surgery Vol. 33, No. 4 ( 2023-04), p. 1040-1048
    Details
    In: Obesity Surgery, Springer Science and Business Media LLC, Vol. 33, No. 4 ( 2023-04), p. 1040-1048
    Abstract: There are very few studies that have compared the short-term outcomes of laparoscopic Roux-en-Y gastric bypass (LRYGB) and laparoscopic sleeve gastrectomy (LSG). Among short-term outcomes, hospital readmission after these procedures is an area for quality enhancement and cost reduction. In this study, we compared 30-day readmission rates after LSG and LRYGB through analyzing a nationalized dataset. In addition, we identified the reasons of readmission. Materials and Methods The current study was a retrospective analysis of data from National Surgical Quality Improvement Program (NSQIP) All adult patients, ≥ 18 years of age and who had LSG or LRYGB during 2014 to 2019 were included. Current Procedural Terminology (CPT) codes were used to identify the procedures. Multivariate logistic regressions were used to calculate propensity score adjusted odds ratios (ORs) for all cause 30-day re-admissions. Results There were 109,900 patients who underwent laparoscopic bariatric surgeries (67.5% LSG and 32.5% LRYGB). Readmissions were reported in 4168 (3.8%) of the patients and were more common among RYGB recipients compared to LSG (5.6% versus 2.9%, P   〈  0.001). The odds of 30-day readmissions were significantly higher among LRYGB group compared to LSG group (AOR, 2.20; 95% CI; 1.83, 2.64). In addition, variables such as age, chronic obstructive pulmonary disease, hypertension, bleeding disorders, blood urea nitrogen, SGOT, alkaline phosphatase, hematocrit, and operation time were significantly predicting readmission rates. Conclusions Readmission rates were significantly higher among those receiving LRYGB, compared to LSG. Readmission was also affected by many patient factors. The factors could help patients and providers to make informed decisions for selecting appropriate procedures. Graphical Abstract
    Type of Medium: Online Resource
    ISSN: 0960-8923 , 1708-0428
    URL: Article
    DOI: 10.1007/s11695-022-06446-6
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2087903-9
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  • 2
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    Abstract 15868: Prevalence and Effects of Acute Myocardial Infarction on Hospital Outcomes Among COVID-19 Patients
    Ovid Technologies (Wolters Kluwer Health) ; 2022
    In:  Circulation Vol. 146, No. Suppl_1 ( 2022-11-08)
    Details
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 146, No. Suppl_1 ( 2022-11-08)
    Abstract: Introduction: In the US, there are approximately 84 million cases of COVID-19 accounting for 1 million deaths. Though there are not many studies, acute myocardial infarction (AMI) is one of the most lethal complications of COVID-19. In this study, we looked for occurrence of AMI and its effects on hospital outcomes among COVID-19 patients. Hypothesis: AMI among COVID-19 hospitalizations could worsen the levels of adverse in-hospital outcomes. Methods: Data from the 2020 California State Inpatient Database was used retrospectively. All COVID-19 hospitalizations with age ≥18 years were included in the analyses. Adverse hospital outcomes included in-hospital mortality, prolonged length of stay, vasopressor use, mechanical ventilation, and ICU admission. Prolonged length of stay included any hospital length of stay ≥75th percentile. We explored for differences in adverse hospital outcomes between those with and without AMI. Multivariate logistic regression analyses were used to understand the strength of these associations after adjusting for cofactors. Results: Our analysis had a total of 94,114 COVID-19 hospitalizations and 1548 (1.6%) had AMI. Adverse hospital outcomes such as mortality (43.2% versus 10.8%, P 〈 0.001), prolonged length of stay (39.9% versus 28.2%, P 〈 0.001), vasopressor use (7.8% versus 2.1%, P 〈 0.001), mechanical ventilation (35.0% versus 9.7%, P 〈 0.001), and ICU admission (33.0% versus 9.4%, P 〈 0.001) were significantly higher among COVID-19 hospitalizations with AMI. The odds of adverse outcomes such as mortality (aOR, 3.90, 95% CI: 3.48-4.36), prolonged length of stay (aOR, 1.23, 95% CI: 1.10-1.37), vasopressor use (aOR, 3.71, 95% CI: 3.30-4.17), mechanical ventilation (aOR, 2.71, 95% CI: 2.21-3.32), and ICU admission (aOR, 3.51, 95% CI: 3.12-3.96) were significantly more prevalent among COVID-19 hospitalizations with AMI. Conclusions: In spite of very low prevalence of AMI among COVID-19 hospitalizations, we observed a substantially greater risk of adverse hospital outcomes and mortality. The hyperinflammatory response of COVID-19 could additionally worsen the factors precipitating mortality among AMI. COVID-19 patients with AMI should be aggressively treated to improve hospital outcomes.
    Type of Medium: Online Resource
    ISSN: 0009-7322 , 1524-4539
    URL: Article
    DOI: 10.1161/circ.146.suppl_1.15868
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2022
    detail.hit.zdb_id: 1466401-X
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  • 3
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    Burden of maternal and fetal outcomes among pregnant cancer survivors during delivery hospitalizations in the United States
    Springer Science and Business Media LLC ; 2022
    In:  Scientific Reports Vol. 12, No. 1 ( 2022-06-15)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-06-15)
    Abstract: Existing studies on pregnancy-related outcomes among cancer survivors are limited by sample size or specificity of the cancer type. This study estimated the burden of adverse maternal and fetal outcomes among pregnant cancer survivors using a national database. This study was a retrospective analysis of National Inpatient Sample collected during 2010–2014. Multivariate regression models were used to calculate odds ratios for maternal and fetal outcomes. The study included a weighted sample of 64,506 pregnant cancer survivors and 18,687,217 pregnant women without cancer. Pregnant cancer survivors had significantly higher odds for death during delivery hospitalization, compared to pregnant women without cancer (58 versus 5 deaths per 100,000 pregnancies). They also had higher odds of severe maternal morbidity (aOR 2.00 [95% CI 1.66–2.41]), cesarean section (aOR 1.27 [95% CI 1.19–1.37] ), labor induction (aOR 1.17 [95% CI 1.07–1.29]), pre-eclampsia (aOR 1.18 [95% CI 1.02–1.36] ), preterm labor (aOR 1.55 [95% CI 1.36–1.76]), chorioamnionitis (aOR 1.45 [95% CI 1.15–1.82] ), postpartum infection (aOR 1.68 [95% CI 1.21–2.33]), venous thromboembolism (aOR 3.62 [95% CI 2.69–4.88] ), and decreased fetal movements (aOR 1.67 [95% CI 1.13–2.46]). This study showed that pregnancy among cancer survivors constitutes a high-risk condition requiring advanced care and collective efforts from multiple subspecialties.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-022-13852-4
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2615211-3
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  • 4
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    Recent Trends in Diabetes-Associated Hospitalizations in the United States
    MDPI AG ; 2022
    In:  Journal of Clinical Medicine Vol. 11, No. 22 ( 2022-11-09), p. 6636-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 11, No. 22 ( 2022-11-09), p. 6636-
    Abstract: The purpose of this study was to examine trends in diabetes-related hospitalizations over the period 2010 to 2019 using Nationwide Inpatient Sample (NIS) to facilitate informed policies regarding diabetes-related prevention and management. Between 2010 and 2019, there were 304 million hospitalizations above 18 years of age, of which 78 million were diabetes-associated hospitalizations. The overall population-adjusted diabetes hospitalizations significantly increased from 3079.0 to 3280.8 per 100,000 US population (relative increase, 6.6%, Ptrend 〈 0.028). Age-stratified analysis showed that hospitalizations significantly increased for 18–29 years (relative increase, 7.8%, Ptrend 〈 0.001) while age- and gender-stratified analysis showed that diabetes hospitalization significantly increased for 18–29-year males (relative increase, 18.1%, Ptrend 〈 0.001). Total hospitalization charge increased from 97.5 billion USD in 2010 to 132.0 billion USD in 2019 (relative increase, 35.4%, Ptrend 〈 0.001). Our study’s findings suggest that diabetes-associated hospitalizations will continue to increase in the future because recent evidence indicates a reappearance of diabetes complications. It is important to screen, prevent, and control diabetes at a younger age based on the trends observed in our study.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm11226636
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662592-1
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  • 5
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    Emerging Evidence on the Effects of Dietary Factors on the Gut Microbiome in Colorectal Cancer
    Frontiers Media SA ; 2021
    In:  Frontiers in Nutrition Vol. 8 ( 2021-10-11)
    Details
    In: Frontiers in Nutrition, Frontiers Media SA, Vol. 8 ( 2021-10-11)
    Abstract: Dietary factors have important role in modulating the gut microbiome, which in-turn regulates the molecular events in colonic mucosa. The composition and resulting metabolism of the gut microbiome are decisive factors in colorectal cancer (CRC) tumorigenesis. Altered gut microbiome is associated with impaired immune response, and the release of carcinogenic or genotoxic substances which are the major microbiome-induced mechanisms implicated in CRC pathogenesis. Diets low in dietary fibers and phytomolecules as well as high in red meat are important dietary changes which predispose to CRC. Dietary fibers which reach the colon in an undigested form are further metabolized by the gut microbiome into enterocyte friendly metabolites such as short chain fatty acid (SCFA) which provide anti-inflammatory and anti-proliferative effects. Healthy microbiome supported by dietary fibers and phytomolecules could decrease cell proliferation by regulating the epigenetic events which activate proto-oncogenes and oncogenic pathways. Emerging evidence show that predominance of microbes such as Fusobacterium nucleatum can predispose the colonic mucosa to malignant transformation. Dietary and lifestyle modifications have been demonstrated to restrict the growth of potentially harmful opportunistic organisms. Synbiotics can protect the intestinal mucosa by improving immune response and decreasing the production of toxic metabolites, oxidative stress and cell proliferation. In this narrative review, we aim to update the emerging evidence on how diet could modulate the gut microbial composition and revive colonic epithelium. This review highlights the importance of healthy plant-based diet and related supplements in CRC prevention by improving the gut microbiome.
    Type of Medium: Online Resource
    ISSN: 2296-861X
    URL: Article
    DOI: 10.3389/fnut.2021.718389
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2776676-7
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  • 6
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    Hospitalization rates from radiotherapy complications in the United States
    Springer Science and Business Media LLC ; 2022
    In:  Scientific Reports Vol. 12, No. 1 ( 2022-03-14)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-03-14)
    Abstract: Hospitalizations due to radiotherapy (RT) complications result in significant healthcare expenditures and adversely affect the quality of life of cancer patients. Using a nationally representative dataset, the objective of this study is to identify trends in the incidence of these hospitalizations, their causes, and the resulting financial burden. Data from the National Inpatient Sample was retrospectively analyzed from 2005 to 2016. RT complications were identified using ICD-9 and ICD-10 external cause-of-injury codes. The hospitalization rate was the primary endpoint, with cost and in-hospital death as secondary outcomes. 443,222,223 weighted hospitalizations occurred during the study period, of which 482,525 (0.11%) were attributed to RT. The 3 most common reasons for RT-related hospitalization were cystitis (4.8%, standard error [SE] = 0.09), gastroenteritis/colitis (3.7%, SE = 0.07), and esophagitis (3.5%, SE = 0.07). Aspiration pneumonitis (1.4-fold) and mucositis (1.3-fold) had the highest relative increases among these hospitalizations from 2005 to 2016, while esophagitis (0.58-fold) and disorders of the rectum and anus were the lowest (0.67-fold). The median length of stay of patient for hospitalization for RT complications was 4.1 (IQR, 2.2–7.5) days and the median charge per patient was $10,097 (IQR, 5755–18,891) and the total cost during the study period was $4.9 billion. Hospitalization for RT-related complications is relatively rare, but those that are admitted incur a substantial cost. Use of advanced RT techniques should be employed whenever possible to mitigate the risk of severe toxicity and therefore reduce the need to admit patients.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-022-08491-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2615211-3
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  • 7
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    Abstract 15686: Prevalence and Effects of Deep Vein Thrombosis on Hospital Outcomes Among COVID-19 Patients
    Ovid Technologies (Wolters Kluwer Health) ; 2022
    In:  Circulation Vol. 146, No. Suppl_1 ( 2022-11-08)
    Details
    In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 146, No. Suppl_1 ( 2022-11-08)
    Abstract: Introduction: Current estimates show that, globally, there are 531 million cases and 6.3 million deaths due to COVID-19. Studies have shown that COVID-19 could lead to deep vein thrombosis (DVT) resulting in increased morbidity and mortality. In this study we sought to estimate the prevalence of DVT among COVID-19 hospitalizations as well as its effects on hospital outcomes using a large administrative database. Hypothesis: The adverse in-hospital outcomes of COVID-19 will be significantly higher among DVT hospitalizations. Methods: We conducted a retrospective analysis of the 2020 California State Inpatient Database. All hospitalizations with age 18 and above and primary diagnosis of COVID-19 were included for the study. They were classified into those with and without DVT. The main outcomes of the study were in-hospital mortality, prolonged length of stay, vasopressor use, mechanical ventilation, and ICU admission. Length of stay ≥75th percentile was grouped as prolonged length of stay. Multivariate logistic regressions with covariate adjustments were conducted to compare COVID-19 related outcomes between those with and without DVT. Results: We included a total of 94,114 primary COVID-19 hospitalizations for the analysis. Among them 1575 (1.7%) had DVT. The prevalence of mortality (27.5% versus 11.1%, P 〈 0.001), prolonged length of stay (62.2% versus 27.8%, P 〈 0.001), vasopressor use (7.9% versus 2.1%, P 〈 0.001), mechanical ventilation (36.2% versus 9.7%, P 〈 0.001), and ICU admission (35.7% versus 9.3%, P 〈 0.001) were significantly higher among those with DVT. After adjusting for covariates, regression analysis showed that those with DVT had significantly greater odds for mortality (aOR, 2.34, 95% CI: 2.07-2.65), prolonged length of stay (aOR, 3.51, 95% CI: 3.16-3.91), vasopressor use (aOR, 4.23, 95% CI: 3.78-4.74), mechanical ventilation (aOR, 2.90, 95% CI: 2.38-3.53), and ICU admission (aOR, 4.32, 95% CI: 3.85-4.84). Conclusions: In our cohort, only few COVID-19 hospitalizations had a diagnosis of DVT. However, among those with DVT, the risk for adverse outcomes were significantly higher. Since DVT among COVID-19 is uncommon but associated with adverse hospital outcomes, healthcare providers should promptly monitor for DVT and manage it.
    Type of Medium: Online Resource
    ISSN: 0009-7322 , 1524-4539
    URL: Article
    DOI: 10.1161/circ.146.suppl_1.15686
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2022
    detail.hit.zdb_id: 1466401-X
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  • 8
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    Relationship between insurance status and interhospital transfers among cancer patients in the United States
    Springer Science and Business Media LLC ; 2022
    In:  BMC Cancer Vol. 22, No. 1 ( 2022-12)
    Details
    In: BMC Cancer, Springer Science and Business Media LLC, Vol. 22, No. 1 ( 2022-12)
    Abstract: The relationship between insurance status and interhospital transfers has not been adequately researched among cancer patients. Hence this study aimed for understanding this relationship using a nationally representative database. Methods A retrospective analysis was conducted using National Inpatient Sample (NIS) data collected during 2010–2016 and included all cancer hospitalization between 18 and 64 years of age. Interhospital transfers were compared based on insurance status (Medicare, Medicaid, private, and uninsured). Weighted multivariable logistic regressions were used to calculate the odds of interhospital transfers based on insurance status, after adjusting for many covariates. Results There were 3,580,908 weighted cancer hospitalizations, of which 72,353 (2.02%) had interhospital transfers. Uninsured patients had significantly higher rates of interhospital transfers, compared to those with Medicare ( P  = 0.005) and private insurance ( P   〈  0.001). Privately insured patients had significantly lower rates of interhospital transfers, compared to those with Medicare ( P   〈  0.001) and Medicaid ( P   〈  0.001). Logistic regression analyses showed that the odds of having interhospital transfers were significantly higher among uninsured (adjusted odds ratio [aOR] , 1.57, 95% CI: 1.45–1.69), Medicare (aOR, 1.38, 95% CI: 1.32–1.45) and Medicaid (aOR, 1.23, 95% CI: 1.16–1.30) patients when compared to those with private insurance coverages. Conclusion Among cancer patients, uninsured and Medicare and Medicaid beneficiaries were more likely to experience interhospital transfers. In addition to medical reasons, factors such as affordability and socioeconomic status are influencing interhospital transfer decisions, indicating existing healthcare disparities. Further studies should focus on identifying the causal associations between factors explored in this study as well as additional unexplored factors.
    Type of Medium: Online Resource
    ISSN: 1471-2407
    URL: Article
    DOI: 10.1186/s12885-022-09242-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2041352-X
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  • 9
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    Abstract 156: Insights From Meta-analysis Of Studies Using Machine Learning To Predict Mortality, Readmission, Or Other Outcomes Among Heart Failure Patients
    Ovid Technologies (Wolters Kluwer Health) ; 2022
    In:  Circulation: Cardiovascular Quality and Outcomes Vol. 15, No. Suppl_1 ( 2022-05)
    Details
    In: Circulation: Cardiovascular Quality and Outcomes, Ovid Technologies (Wolters Kluwer Health), Vol. 15, No. Suppl_1 ( 2022-05)
    Abstract: Developing and implementing analytical models for predicting mortality or readmission related outcomes among heart failure (HF) patients are challenging. In this study, we used meta-analyses of reported predictive models to assess what machine learning (ML) has been able to accomplish in this field, by evaluating the ML model performance for studies in HF. We performed a literature search using Google Scholar, Web of Science and PubMed. The studies reporting AUC and 95% CI for various models were included. In addition, total participants, year of publication, type of analytical method (logistic regression, RF, etc.) and type of outcome (mortality, readmission, etc.) were extracted. We combined effect sizes using random effects (RF) model, and tested for heterogeneity, and publication bias. 12 studies were included in the analysis (patients= 123,832; AUC=15, with outcome mortality =17,471, readmission=15,703, hospitalization=67,523). Combined mean AUC was 0.77 (95% CI: 0.72, 0.82). Test of heterogeneity showed high variation between studies (I2=98.9%). Egger’s test intercept was 5.2 (95% CI: -4.2, 14.7, p 〉 .25) indicating no small study effects/bias. Meta regression showed newer publications provide better AUC values (p 〈 0.03). In subgroup analysis, the pooled AUC for readmission, hospitalization, and mortality groups were 0.71, 0.80, and 0.78 respectively. The highest individual AUC was from neural networks (NN) predicting hospitalization with AUC 0.96 and lowest was from RF predicting readmission with AUC 0.65. Presented models were diverse, ranking in quality from fair to very good, and being varied for different clinical outcomes among HF patients. Situation known from studies using classical statistical methods holds also for methods using ML, with better predictive values for hospitalizations, and lower for other outcomes. Methods using NN and methods using higher numbers of variables performed very well and had highest predictive power.
    Type of Medium: Online Resource
    ISSN: 1941-7713 , 1941-7705
    URL: Article
    DOI: 10.1161/circoutcomes.15.suppl_1.156
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2022
    detail.hit.zdb_id: 2453882-6
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  • 10
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    Abstract 263: Insights From Meta-analysis Of Studies Using Machine Learning To Predict Mortality Or Acute Kidney Injury After Coronary Artery Bypass Graft
    Ovid Technologies (Wolters Kluwer Health) ; 2022
    In:  Circulation: Cardiovascular Quality and Outcomes Vol. 15, No. Suppl_1 ( 2022-05)
    Details
    In: Circulation: Cardiovascular Quality and Outcomes, Ovid Technologies (Wolters Kluwer Health), Vol. 15, No. Suppl_1 ( 2022-05)
    Abstract: Objective: Machine learning (ML) may enhance prediction of outcomes such as mortality or acute kidney injury (AKI) among cardiac patients after coronary artery bypass graft (CABG). In this study, we used meta-analyses of reported ML models to assess what ML has been able to accomplish in this field, by evaluating the model performance in studies with CABG patients. Methods: We performed a literature search using Google Scholar and included studies that reported AUC and 95% CI for various models in our analysis. In addition, total participants, year of publication, type of analytical method (gradient boosting, random forest, etc.) and type of outcome (mortality or AKI) were extracted. We combined effect sizes using random effects model, and tested for heterogeneity, and publication bias. Results: 5 models from 5 studies were included in the analysis (patients= 35,152; with outcome mortality =3,080, AKI=933). Combined mean AUC was 0.796 (95% CI: 0.776, 0.815). Test of heterogeneity showed high variation between studies (I 2 = 66.7%). Egger’s test intercept was -1.03 (95% CI: -7.22, 5.17, p 〉 .25) indicating no small study bias. Meta regression showed newer publications had a positive association ( coef = 0.003) and number of variables in the study had a negative association with higher AUC values ( coef = -0.0002). In subgroup analysis, the pooled AUC values for mortality and AKI groups were 0.795 and 0.805 respectively. The highest individual AUC was from ensemble model predicting AKI with AUC 0.84 and lowest was from gradient boosting model predicting mortality with AUC 0.77. Conclusion: Among the presented models for CABG ensemble methods performed well, but surprisingly methods with lesser number of variables tended to have higher predictive power. In near future, ML-based models may form the basis to build intelligent decision support systems for patient selection and risk stratification prior to CABG and could be applied to other cardiac surgeries.
    Type of Medium: Online Resource
    ISSN: 1941-7713 , 1941-7705
    URL: Article
    DOI: 10.1161/circoutcomes.15.suppl_1.263
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2022
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