Variation In Hospital Mortality Research Articles

ASO Visual Abstract: Variation in Hospital Mortality After Complex Cancer Surgery—Patient, Volume, Hospital, or Social Determinants?

ASO Visual Abstract: Variation in Hospital Mortality After Complex Cancer Surgery—Patient, Volume, Hospital, or Social Determinants?

ASO Author Reflections: Variation in Hospital Mortality After Complex Cancer Surgery: Patient, Volume, Hospital, or Social Determinants?

ASO Author Reflections: Variation in Hospital Mortality After Complex Cancer Surgery: Patient, Volume, Hospital, or Social Determinants?

Variation in Hospital Mortality After Complex Cancer Surgery: Patient, Volume, Hospital or Social Determinants?

IntroductionWe sought to define the individual contributions of patient characteristics (PCs), hospital characteristics (HCs), case volume (CV), and social determinants of health (SDoH) on in-hospital mortality (IHM) after complex cancer surgery.MethodsThe California Department of Health Care Access and Information database identified patients who underwent esophagectomy (ES), pneumonectomy (PN), pancreatectomy (PD), or proctectomy (PR) for a malignant diagnosis between 2010 and 2020. Multi-level multivariable regression was performed to assess the proportion of variance explained by PCs, HCs, CV and SDoH on IHM.ResultsA total of 52,838 patients underwent cancer surgery (ES: n = 2,700, 5.1%; PN: n = 30,822, 58.3%; PD: n = 7530, 14.3%; PR: n = 11,786, 22.3%) across 294 hospitals. The IHM for the overall cohort was 1.7% and varied from 4.4% for ES to 0.8% for PR. On multivariable regression, PCs contributed the most to the variance in IHM (overall: 32.0%; ES: 21.6%; PN: 28.0%; PD: 20.3%; PR: 39.9%). Among the overall cohort, CV contributed 2.4%, HCs contributed 1.3%, and SDoH contributed 1.2% to the variation in IHM. CV was the second highest contributor to IHM among ES (5.3%), PN (5.3%), and PD (5.9%); however, HCs were a more important contributor among patients who underwent PR (8.0%). The unexplained variance in IHM was highest among ES (72.4%), followed by the PD (67.5%) and PN (64.6%) patient groups.ConclusionsPCs are the greatest underlying contributor to variations in IHM following cancer surgery. These data highlight the need to focus on optimizing patients and exploring unexplained sources of IHM to improve quality of surgical care.

Impact of Sedation Practices on Mortality in COVID-19-Associated Adult Respiratory Distress Syndrome Patients: A Multicenter Retrospective Descriptive Study.

Background: Reduction in sedation exposure is an important metric in intensive care unit (ICU) patients. However, challenges arose during the coronavirus disease-2019 (COVID-19) pandemic in adhering to this practice, driven by concerns on transmission and disease severity issues. Accordingly, diverse sedation approaches emerged, although the effect on mortality has not been studied thoroughly. Methods: Retrospective cohort study in the medical ICU of seven hospitals within a major Health System in Northeast Ohio. We included all adult patients admitted with COVID-19 requiring invasive mechanical ventilation (IMV) from March 2020 to December 2021. Results: Study included 2394 COVID-19 patients requiring IMV. Across waves, sample included 55-63% male subjects, with an average age of 61-68 years (P < 0.001), Acute Physiologic and Chronic Health Evaluation (APACHE)-III score 65.8-68.9 (P = 0.37), median IMV duration 8-10 days (P = 0.14), and median ICU duration 9.8-11.6 days (P = 0.084). Propofol remained the primary sedative (84-92%; P = 0.089). Ketamine use increased from the first (9.7%) to fourth (19%) wave (P = 0.002). Midazolam use decreased from the first (27.4%) to third (9.4%) wave (P = 0.001). Dexmedetomidine use declined from 35% to 27-28% (P = 0.002) after the first wave. A multivariable regression analysis indicated clinical variables explained 34% of the variation in hospital mortality (R2). Factors associated with higher mortality included age [aOR = 1.059 (95% CI 1.049-1.069); P < 0.001], COVID-19 wave, especially fourth wave [aOR = 2.147, (95% CI 1.370-3.365); P = 0.001], and higher number of vasopressors [aOR = 31.636, (95% CI 17.603-56.856); P < 0.001]. Addition of sedative medications to a second model led to an increase in the R2 by only 1.6% to 35.6% [aOR = 1 (95% CI 1-1); P > 0.05] for propofol, ketamine, and midazolam. Dexmedetomidine demonstrated a decrease in the odds of mortality [aOR = 0.96 (95% CI 0.94-0.97); P < 0.001]. Conclusion: Mortality in critical COVID-19 patients was mostly driven by illness severity, and the choice of sedation might have minimal impact when other factors are controlled.

Understanding Variation in In-hospital Mortality After Major Surgery in the United States.

We aimed to quantify the contributions of patient characteristics (PC), hospital structural characteristics (HC), and hospital operative volumes (HOV) to in-hospital mortality (IHM) after major surgery in the United States (US). The volume-outcome relationship correlates higher HOV with decreased IHM. However, IHM after major surgery is multifactorial, and the relative contribution of PC, HC, and HOV to IHM after major surgery is unknown. Patients undergoing major pancreatic, esophageal, lung, bladder, and rectal operations between 2006 and 2011 were identified from the Nationwide Inpatient Sample linked to the American Hospital Association survey. Multilevel logistic regression models were constructed using PC, HC, and HOV to calculate attributable variability in IHM for each. Eighty thousand nine hundred sixty-nine patients across 1025 hospitals were included. Postoperative IHM ranged from 0.9% for rectal to 3.9% for esophageal surgery. Patient characteristics contributed most of the variability in IHM for esophageal (63%), pancreatic (62.9%), rectal (41.2%), and lung (44.4%) operations. HOV explained < 25% of variability for pancreatic, esophageal, lung, and rectal surgery. HC accounted for 16.9% and 17.4% of the variability in IHM for esophageal and rectal surgery. Unexplained variability in IHM was high in the lung (44.3%), bladder (39.3%), and rectal (33.7%) surgery subgroups. Despite recent policy focus on the volume-outcome relationship, HOV was not the most important contributor to IHM for the major organ surgeries studied. PC remains the largest identifiable contributor to hospital mortality. Quality improvement initiatives should emphasize patient optimization and structural improvements, in addition to investigating the yet unexplained sources contributing to IHM.

Is there variation between hospitals within each region in postoperative mortality for lung cancer surgery in France? A nationwide study from 2013 to 2020.

The practice of thoracic surgery for lung cancer is subject to authorization in France. We evaluated the performance of hospitals using 30-day post-operative mortality as a quality indicator, estimating its distribution within each region and measuring its variability between regions. All data for patients who underwent pulmonary resection for lung cancer in France (2013-2020) were collected from the national hospital administrative database. Thirty-day mortality was defined as any patient who died in hospital (including transferred patients) within the first 30 days after the operation and those who died later during the initial hospitalization. The Standardized Mortality ratio (SMR) was the smoothed, adjusted, hospital-specific mortality rate divided by the expected mortality. To describe the variation in hospital mortality between hospitals in each region, we used different commonly used indicators of variation such as coefficients of variation (CV), interquartile interval or range (IQR), extreme ratio, and systematic component of variance (SCV). In 2013-2020, 87,232 patients underwent lung resection for cancer in France. The number of deaths was 2,537, a rate of 2.91%. The median SMR of 199 hospitals was 0.99 with an IQR of 0.86 to 1.18 and a CV of 0.25. Among the regions that had the most hospitals performing lung resections for cancer, the extreme ratio was >2, which means that the maximum value is twice as high as the minimum value. The SCV between hospitals was >10 for two of these regions, which is considered indicative of very high variation. For the other regions (with few hospitals performing lung resections for cancer), the variation between hospitals was lower. Globally, the variability between regions concerning the SMR was moderate, 6% of the variance was due to differences across regions. On the contrary, the hospital volume was significantly related to the SMR (p = 0.003) with a negative linear trend, whatever the region. This work shows significant differences in the practices of the various hospitals within regions. However, overall, the variability in the 30-day mortality rate between regions was moderate. Our findings raises questions regarding the regionalization of major surgical procedures in France.

A comprehensive analysis of temporal trends of between-hospital variation in mortality, readmission and length of stay using logistic regression

Despite the benefits of studying multiple patient outcomes together, research on between-hospital variation has often focused on single outcomes or disease-specific study populations. In this study we examined nationwide temporal trends and between-hospital variation in in-hospital mortality, 30-day readmissions and length-of-stay above the All-Patient-Refined Diagnoses-Related-Group (APR-DRG)-specific 90th percentile (pLOS). We modelled 13,660,187 admissions derived from an administrative database occurring between 2008 and 2018 in 90 (89%) Belgian acute-care hospitals. We applied an APR-DRG-specific logistic regression to study temporal trends in outcomes, hospital-level associations between outcomes, associations of outcomes with hospitals characteristics, and to evaluate how many and which APR-DRGs explained between-hospital variation. Our proposed analytical model managed to achieve novel insights into healthcare quality of care, illustrating the high potential administrative databases can provide. It was revealed that between-hospital variation in outcomes is likely due to systemic hospital factors. This is illustrated by the fact that baseline bottom-performing hospitals remained underperforming throughout the study period and vice versa. APR-DRG-specific between-hospital variation assessments further confirmed this. When hospitals have overall outcome ratios that significantly deviate from the benchmark, this seems to be driven by a considerable number of APR-DRGs, comprising a diverse set of pathologies. This urges a healthcare policy reform wherein longitudinal follow-up and benchmarking of patient outcomes should become the starting point towards targeted quality improvement interventions.

Variation in in-hospital mortality and its association with percutaneous coronary intervention-related bleeding complications: A report from nationwide registry in Japan.

Large-scale registries have demonstrated that in-hospital mortality after percutaneous coronary intervention (PCI) varies widely across institutions. However, whether this variation is related to major procedural complications (e.g., bleeding) is unclear. In this study, institutional variation in in-hospital mortality and its association with PCI-related bleeding complications were investigated. We analyzed 388,866 procedures at 718 hospitals performed from 2017 to 2018, using data from a nationwide PCI registry in Japan. Hospitals were stratified into quintiles according to risk-adjusted in-hospital mortality (very low, low, medium, high, and very high). Incidence of bleeding complications, defined as procedure-related bleeding events that required a blood transfusion, and in-hospital mortality in patients who developed bleeding complications were calculated for each quintile. Overall, 4,048 (1.04%) in-hospital deaths and 1,535 (0.39%) bleeding complications occurred. Among patients with bleeding complications, 270 (17.6%) died during hospitalization. In-hospital mortality ranged from 0.22% to 2.46% in very low to very high mortality hospitals. The rate of bleeding complications varied modestly from 0.27% to 0.57% (odds ratio, 1.95; 95% confidence interval, 1.58-2.39). However, mortality after bleeding complications markedly increased by quintile and was 6-fold higher in very high mortality hospitals than very low mortality hospitals (29.0% vs. 4.8%; odds ratio, 12.2; 95% confidence interval, 6.90-21.7). In conclusion, institutional variation in in-hospital mortality after PCI was associated with procedure-related bleeding complications, and this variation was largely driven by differences in mortality after bleeding complications rather than difference in their incidence. These findings underscore the importance of efforts toward reducing not only bleeding complications but also, even more importantly, subsequent mortality once they have occurred.

Variation in in-hospital mortality and its association with procedure-related bleeding complications: a report from nationwide percutaneous coronary intervention registry in japan

Abstract Background Large-scale registries have demonstrated that in-hospital mortality after percutaneous coronary intervention (PCI) varies widely across institutions; however, the underlying reasons are not fully understood. Bleeding has been regarded as one of the most serious PCI-related complications associated with increase in mortality. To date, reducing bleeding complications have tended to be addressed, though appropriate care for patients who developed bleeding complications may be also important. Therefore, we sought to investigate which was more involved in the inter-institutional variations in in-hospital mortality. Methods We analyzed 388,866 procedures at 718 hospitals performed from 2017 to 2018, using data from a representative nationwide PCI registry in Japan. A bleeding complication was defined as post-procedural bleeding that required a blood transfusion. We first divided the hospitals into quintiles according to their risk-adjusted in-hospital mortality (very low, low, medium, high, and very high). Then, the bleeding complication rate and in-hospital mortality rate among patients who developed bleeding complications were calculated for each quintile. Results In total, 4,048 (1.04%) in-hospital deaths and 1,535 (0.39%) bleeding complications occurred. In-hospital mortality varied widely from 0.22% to 2.46% in very-low- to very-high-mortality hospitals. The bleeding complication rate also increased modestly from 0.27% to 0.57% (odds ratio, 1.95; 95% confidence interval, 1.58–2.39). However, the mortality rate among patients who developed bleeding complications markedly increased by quintile; the mortality rate of very-high-morality hospitals was 6-fold higher than that of very-low-mortality hospitals (29.0% vs. 4.8%, respectively; odds ratio, 12.2; 95% confidence interval, 6.9–21.7). Conclusions Wide variation in in-hospital mortality was observed within the Japanese nationwide PCI registry, and this variation was largely driven by differences in mortality among patients with bleeding complications, rather than crude incidence of bleeding complications. These results underscore the importance of efforts toward reducing not only bleeding complications but also, even more importantly, mortality once bleeding has occurred. Funding Acknowledgement Type of funding sources: None.

Real-world implementation challenges in low-resource settings

Real-world implementation challenges in low-resource settings

Variability in COVID-19 in-hospital mortality rates between national health service trusts and regions in England: A national observational study for the Getting It Right First Time Programme

BackgroundA key first step in optimising COVID-19 patient outcomes during future case-surges is to learn from the experience within individual hospitals during the early stages of the pandemic. The aim of this study was to investigate the extent of variation in COVID-19 outcomes between National Health Service (NHS) hospital trusts and regions in England using data from March–July 2020.MethodsThis was a retrospective observational study using the Hospital Episode Statistics administrative dataset. Patients aged ≥ 18 years who had a diagnosis of COVID-19 during a hospital stay in England that was completed between March 1st and July 31st, 2020 were included. In-hospital mortality was the primary outcome of interest. In secondary analysis, critical care admission, length of stay and mortality within 30 days of discharge were also investigated. Multilevel logistic regression was used to adjust for covariates.FindingsThere were 86,356 patients with a confirmed diagnosis of COVID-19 included in the study, of whom 22,944 (26.6%) died in hospital with COVID-19 as the primary cause of death. After adjusting for covariates, the extent of the variation in-hospital mortality rates between hospital trusts and regions was relatively modest. Trusts with the largest baseline number of beds and a greater proportion of patients admitted to critical care had the lowest in-hospital mortality rates.InterpretationThere is little evidence of clustering of deaths within hospital trusts. There may be opportunities to learn from the experience of individual trusts to help prepare hospitals for future case-surges.

Hospital-Level Variation in Death for Critically Ill Patients with COVID-19.

Rationale: Variation in hospital mortality has been described for coronavirus disease (COVID-19), but the factors that explain these differences remain unclear.Objective: Our objective was to use a large, nationally representative data set of critically ill adults with COVID-19 to determine which factors explain mortality variability.Methods: In this multicenter cohort study, we examined adults hospitalized in ICUs with COVID-19 at 70 U.S. hospitals between March and June 2020. The primary outcome was 28-day mortality. We examined patient-level and hospital-level variables. Mixed-effect logistic regression was used to identify factors associated with interhospital variation. The median odds ratio was calculated to compare outcomes in higher- versus lower-mortality hospitals. A gradient-boosted machine algorithm was developed for individual-level mortality models.Measurements and Main Results: A total of 4,019 patients were included, 1,537 (38%) of whom died by 28 days. Mortality varied considerably across hospitals (0–82%). After adjustment for patient- and hospital-level domains, interhospital variation was attenuated (odds ratio decline from 2.06 [95% confidence interval (CI), 1.73–2.37] to 1.22 [95% CI, 1.00–1.38]), with the greatest changes occurring with adjustment for acute physiology, socioeconomic status, and strain. For individual patients, the relative contribution of each domain to mortality risk was as follows: acute physiology (49%), demographics and comorbidities (20%), socioeconomic status (12%), strain (9%), hospital quality (8%), and treatments (3%).Conclusions: There is considerable interhospital variation in mortality for critically ill patients with COVID-19, which is mostly explained by hospital-level socioeconomic status, strain, and acute physiologic differences. Individual mortality is driven mostly by patient-level factors.

Outcomes of Patients With Coronavirus Disease 2019 Receiving Organ Support Therapies: The International Viral Infection and Respiratory Illness Universal Study Registry.

To describe the outcomes of hospitalized patients in a multicenter, international coronavirus disease 2019 registry. Cross-sectional observational study including coronavirus disease 2019 patients hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus-2 infection between February 15, 2020, and November 30, 2020, according to age and type of organ support therapies. About 168 hospitals in 16 countries within the Society of Critical Care Medicine's Discovery Viral Infection and Respiratory Illness University Study coronavirus disease 2019 registry. Adult hospitalized coronavirus disease 2019 patients who did and did not require various types and combinations of organ support (mechanical ventilation, renal replacement therapy, vasopressors, and extracorporeal membrane oxygenation). None. Primary outcome was hospital mortality. Secondary outcomes were discharge home with or without assistance and hospital length of stay. Risk-adjusted variation in hospital mortality for patients receiving invasive mechanical ventilation was assessed by using multilevel models with hospitals as a random effect, adjusted for age, race/ethnicity, sex, and comorbidities. Among 20,608 patients with coronavirus disease 2019, the mean (± sd) age was 60.5 (±17), 11,1887 (54.3%) were men, 8,745 (42.4%) were admitted to the ICU, and 3,906 (19%) died in the hospital. Hospital mortality was 8.2% for patients receiving no organ support (n = 15,001). The most common organ support therapy was invasive mechanical ventilation (n = 5,005; 24.3%), with a hospital mortality of 49.8%. Mortality ranged from 40.8% among patients receiving only invasive mechanical ventilation (n =1,749) to 71.6% for patients receiving invasive mechanical ventilation, vasoactive drugs, and new renal replacement therapy (n = 655). Mortality was 39% for patients receiving extracorporeal membrane oxygenation (n = 389). Rates of discharge home ranged from 73.5% for patients who did not require organ support therapies to 29.8% for patients who only received invasive mechanical ventilation, and 8.8% for invasive mechanical ventilation, vasoactive drugs, and renal replacement; 10.8% of patients older than 74 years who received invasive mechanical ventilation were discharged home. Median hospital length of stay for patients on mechanical ventilation was 17.1 days (9.7-28 d). Adjusted interhospital variation in mortality among patients receiving invasive mechanical ventilation was large (median odds ratio 1.69). Coronavirus disease 2019 prognosis varies by age and level of organ support. Interhospital variation in mortality of mechanically ventilated patients was not explained by patient characteristics and requires further evaluation.

Ethnic and Regional Variation in Hospital Mortality from COVID-19 in Brazil

Background: The COVID-19 pandemic is quickly spreading throughout Brazil, which is rapidly ascending the ranking of countries with the highest number of cases and deaths. A particularly unstable federal regime and fragile socioeconomic situation is likely to have contributed to the impact of the disease. Amid this crisis there is substantial concern in the possible socioeconomic, geopolitical and ethnic inequity of the impact of COVID-19 on the country's particularly diverse population. Methods: We performed a cross-sectional observational study of COVID-19 hospital mortality using observational data from the SIVEP-Gripe dataset. We present descriptive statistics to quantify the COVID-19 pandemic in Brazil. We assess the importance of regional factors such as education, income and health either on a state-by-state basis or by splitting Brazil into a North and a Central-South region. Mixed-effects survival analysis was used to estimate the effects of ethnicity and comorbidity at an individual level in the context of regional variation. Findings: Our results show that, compared to branco comparators, hospitalised pardo and preto Brazilians have significantly higher risk of mortality, with hazard ratios and 95% CI of 1.47 (1.33-1.58) and 1.32 (1.15-1.52), respectively. In particular, pardo ethnicity was the second most important risk factor (after age). We also found that hospitalised Brazilians in North regions tend to have more comorbidities than in the Central-South, with similar proportions between the various ethnic groups. Finally, we found that states in the North have a higher hazard ratio as compared to the Central-South, and that Rio de Janeiro obtained one of the highest hazard ratios, similar to the ones of the more underdeveloped Pernambuco and Amazonas. Interpretation: Our results can be interpreted according to the interplay of two independent, but correlated, effects: i) mortality by COVID-19 increases going North (vertical effect), ii) mortality increases for the pardo and preto population (horizontal effect). We speculate that the vertical effect is driven by increasing levels of comorbidity in Northern regions where levels of socioeconomic development are lower, whereas the horizontal effect may be related to lower levels of healthcare access or availability (including intensive care) for pardo and preto Brazilians. For most states the vertical and horizontal effects are correlated giving a larger cumulative mortality. However, Rio de Janeiro was found to be an outlier to this trend: It has an ethnicity composition (horizontal effect) similar to the states in the North region, despite high levels of socioeconomic development (vertical effect). Our analysis motivates an urgent effort on the part of Brazilian authorities to consider how the national response to COVID-19 can better protect pardo and preto Brazilians as well as the population of poorer states from their higher death risk from SARS-CoV-2 infection.Funding Statement: None.Declaration of Interests: The authors declare no competing interests.

COVID-19 in-patient hospital mortality by ethnicity

There is debate about the extent to which COVID-19 affects ethnic groups differently. We explored if there was variation in hospital mortality in patients with COVID. Mortality rates in 1,276 inpatients in Bradford with test results for COVID-19 were analysed by ethnic group. The age-adjusted risk of dying from COVID-19 was slightly lower in South Asian compared to White British patients. (RR =0.87, 95% CI: 0.41 to 1.84).

Reducing variation in hospital mortality for alcohol-related liver disease in North West England.

Variations in emergency care quality for alcohol-related liver disease (ARLD) have been highlighted. To determine whether introduction of a regional quality improvement (QI) programme was associated with a reduction in potentially avoidable inpatient mortality. Retrospective observational cohort study using hospital administrative data spanning a 1-year period before (2014/2015) and 3years after a QI initiative at seven acute hospitals in North West England. The intervention included serial audit of a bundle of process metrics. An algorithm was developed to identify index ("first") emergency admissions for ARLD (n=3887). We created a standardised mortality ratio (SMR) to compare relative mortality and regression models to examine risk-adjusted odds of death. In 2014/2015, three of seven hospitals had an SMR above the upper control limit ("outliers"). Adjusted odds of death for patients admitted to outlier hospitals was higher than non-outliers (OR 2.13, 95% CI 1.32-3.44, P=0.002). Following the QI programme there was a step-wise reduction in outliers (none in 2017/2018). Odds of death was 67% lower in 2017/2018 compared to 2014/2015 at original outlier hospitals, but unchanged at other hospitals. Process audit performance of outliers was worse than non-outliers at baseline, but improved after intervention. There was a reduction in unexplained variation in hospital mortality following the QI intervention. This challenges the pessimism that is prevalent for achieving better outcomes for patients with ARLD. Notwithstanding the limitations of an uncontrolled observational study, these data provide hope that co-ordinated efforts to drive adoption of evidence-based practice can save lives.

COVID-19 in-patient hospital mortality by ethnicity.

There is debate about the extent to which COVID-19 affects ethnic groups differently. We explored if there was variation in hospital mortality in patients with COVID. Mortality rates in 1,276 inpatients in Bradford with test results for COVID-19 were analysed by ethnic group. The age-adjusted risk of dying from COVID-19 was slightly lower in South Asian compared to White British patients. (RR =0.87, 95% CI: 0.41 to 1.84).

P2653Seasonal variations of myocardial infarction and sex-specific differences in Germany

Abstract Background Ischemic heart disease (IHD) is the most common cause of death with an increasing frequency worldwide. It accounts for approximately 20% of all deaths in Europe and the United States of America. Approximately 1/3 of the IHD patients present with sudden cardiac death. The acute presentation of IHD myocardial infarction (MI) is a life-threatening, serious health problem, which causes substantially morbidity and mortality. It is well established that the onset of MI follows a circadian and seasonal periodicity. Seasonal variation regarding the incidence and the short-term mortality of acute MI was frequently reported, but data about sex-specific differences are sparse. Purpose Thus, our objectives were to investigate seasonal variations of myocardial infarction. Methods We analyzed the impact of seasons on incidence and in-hospital mortality of patients with acute MI in Germany from 2005 to 2015. We included all MI patients (ICD code I21) with an acute MI (, but not those MI patients with a recurrent event in the first 28 days after a previous MI (ICD code I22)), who were hospitalized in Germany between 2005 and 2015, in this analysis (source: RDC of the Federal Statistical Office and the Statistical Offices of the federal states, DRG Statistics 2005–2015, own calculations). Results The nationwide sample comprised 3,008,188 hospitalizations of patients with MI (2005–2015). The annual incidence was 334.7 per 100.000 population. Incidence inclined from 316.3 to 341.6 per 100.000 population per year (β 0.17 [0.10 to 0.24], P&lt;0.001), while in-hospital mortality rate decreased from 14.1% to 11.3% (β −0.29 [−0.30 to −0.28, P&lt;0.001). Overall, 377,028 (12.5%) patients died in-hospital. Seasonal variation of both incidence and in-hospital mortality were of substantial magnitude. Seasonal incidence (86.1 vs. 79.0 per 100.000 population per year, P&lt;0.001) and in-hospital mortality (13.2% vs. 12.1%, P&lt;0.001) were higher in the winter than in the summer saeson. Risk to die in winter was elevated (OR 1.080 (95% CI 1.069–1.091), P&lt;0.001) compared to summer season independently of sex, age and comorbidities. Reperfusion treatment with drug eluting stents and coronary artery bypass graft were more often used in summer. We observed sex-specific differences regarding the seasonal variation of in-hospital mortality: males showed lowest mortality in summer, while females during fall. Low temperature dependency of mortality seems more pronounced in males. Conclusions Incidence of acute MI increased 2005–2015, while in-hospital mortality rate decreased. Seasonal variations of incidence and in-hospital mortality were of substantial magnitude with lowest incidence and lowest mortality in the summer season. Additionally, we observed sex-specific differences regarding the seasonal variation of the in-hospital mortality. Acknowledgement/Funding This study was supported by the German Federal Ministry of Education and Research (BMBF 01EO1503)

Re: Interpersonal and Organizational Dynamics are Key Drivers of Failure to Rescue.

No AccessJournal of UrologyUrological Survey1 Jul 2019Re: Interpersonal and Organizational Dynamics are Key Drivers of Failure to Rescue Matthew J. ResnickMD, MPH, MMHC Matthew J. ResnickMatthew J. Resnick More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000261AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Re: Interpersonal and Organizational Dynamics are Key Drivers of Failure to Rescue." The Journal of Urology, 202(1), p. 21 Reference 1. : Variation in hospital mortality associated with inpatient surgery. N Engl J Med 2009; 361: 1368. Google Scholar © 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 202Issue 1July 2019Page: 21-21 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Matthew J. Resnick More articles by this author Expand All Advertisement Loading ...

Failure to rescue as a source of variation in hospital mortality after rectal surgery: The Italian experience

IntroductionFailure to rescue (FTR) patients from postoperative complications could contribute to the variability in surgical mortality seen among hospitals with different volumes. We sought to examine the impact of complications and FTR on mortality following rectal surgery. MethodsThe National Italian Hospital Discharge Dataset allowed to identify 75,280 patients who underwent rectal surgery between 2002 and 2014. Hospital volume was stratified into tertiles. Rates of major complications, FTR from complications and mortality following rectal surgery were compared. ResultsDuring the study period, both the incidence of complications (2002, 23.7% versus 2014, 21.2%), and FTR decreased overtime (2002, 6.9% versus 2014, 3.8%) (both P < 0.001). The complication rate was 24.4% in low-, 21.6% in intermediate- and 20.4% in high-volume hospitals (P < 0.001). Complications were less common in minimally invasive surgery (MIS) versus open cases (18.2% versus 23.2%; P < 0.001). The most frequent complications included prolonged ileus or small bowel obstruction (5.3%), and anemia requiring blood transfusions (5.3%). The rate of FTR was 5.5%, 5.6% and 3.7% for low-, intermediate- and high-volume hospitals, respectively (P < 0.001). FTR after MIS was 2.6% vs. 5.5% after open surgery (P < 0.001). After accounting for patient and hospital characteristics, patients treated at low-volume hospitals were 23% more likely to die after a complication, compared to patients at high-volume hospitals (OR 1.23, 95%CI 1.13–1.33). ConclusionsHospital volume is the strongest predictor of complication and FTR. The reduction in mortality in high-volume hospitals could be determined by the better ability to rescue patients. These findings support the centralization policy of rectal cancer treatment.