Comparisons of the risk of myopericarditis between COVID-19 patients and individuals receiving COVID-19 vaccines: a population-based study

BackgroundBoth COVID-19 infection and COVID-19 vaccines have been associated with the development of myopericarditis. The objective of this study is to 1) analyze the rates of myopericarditis after COVID-19 infection and COVID-19 vaccination in Hong Kong and 2) compare to the background rates, and 3) compare the rates of myopericarditis after COVID-19 vaccination to those reported in other countries. MethodsThis was a population-based cohort study from Hong Kong, China. Patients with positive RT-PCR test for COVID-19 between 1st January 2020 and 30th June 2021 or individuals who received COVID-19 vaccination until 31st August were included. The main exposures were COVID-19 positivity or COVID-19 vaccination. The primary outcome was myopericarditis. ResultsThis study included 11441 COVID-19 patients from Hong Kong, of whom four suffered from myopericarditis (rate per million: 350; 95% confidence interval [CI]: 140-900). The rate was higher than the pre-COVID-19 background rate in 2020 (rate per million: 61, 95% CI: 55-67) with a rate ratio of 5.73 (95% CI: 2.23-14.73. Compared to background rates, the rate of myopericarditis among vaccinated subjects in Hong Kong was substantially lower (rate per million: 8.6; 95% CI: 6.4-11.6) with a rate ratio of 0.14 (95% CI: 0.10-0.19). The rates of myocarditis after vaccination in Hong Kong are comparable to those vaccinated in the United States, Israel, and the United Kingdom. ConclusionsCOVID-19 infection is associated with a higher rate of myopericarditis whereas COVID-19 vaccination is associated with a lower rate of myopericarditis compared to the background.

Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong

Nonpharmaceutical interventions (NPIs) for contact suppression have been widely used worldwide, which impose harmful burdens on the population and the local economy. The evaluation of alternative NPIs is needed to confront the pandemic with less disruption. By harnessing human mobility data, we develop an agent-based model that can evaluate the efficacies of NPIs with individualized mobility simulations. Based on the model, we propose data-driven targeted interventions to mitigate the COVID-19 pandemic in Hong Kong without city-wide NPIs. We develop a data-driven agent-based model for 7.55 million Hong Kong residents to evaluate the efficacies of various NPIs in the first 80 days of the initial outbreak. The entire territory of Hong Kong is split into 4,905 500m x 500m grids. The model can simulate detailed agent interactions based on the demographics data, public facilities and functional buildings, transportation systems, and travel patterns. The general daily human mobility patterns are adopted from Googles Community Mobility Report. The scenario without any NPIs is set as the baseline. By simulating the epidemic progression and human movement at the individual level, we proposed model-driven targeted interventions, which focus on the surgical testing and quarantine of only a small portion of regions instead of enforcing NPIs in the whole city. The efficacious of common NPIs and the proposed targeted interventions are evaluated by extensive 100 simulations. The proposed model can inform targeted interventions, which are able to effectively contain the COVID-19 outbreak with much lower disruption of the city. It represents a promising approach to sustainable NPIs to help us revive the economy of the city and the world.

Short-term efficacy of Billroth Ⅱ+Braun anastomosis versus Roux-en-Y anastomosis in totally three-dimensional laparoscopic distal gastrectomy / 中华消化外科杂志

Objective:To compare the short-term efficacy of Billroth Ⅱ+Braun anasto-mosis versus Roux-en-Y anastomosis in totally three-dimensional (3D) laparoscopic distal gastrectomy.Methods:The retrospective cohort study was conducted. The clinicopathological data of 140 patients with gastric cancer who were admitted to the First Medical Center of Chinese PLA General Hospital from January 2016 to January 2020 were collected. There were 105 males and 35 females, aged from 23 to 84 years, with a median age of 55 years. Of the 140 patients, 54 patients undergoing totally 3D laparoscopic distal gastrectomy with Billroth Ⅱ+Braun anastomosis were allocated into Billroth Ⅱ+Braun group, and 86 patients undergoing totally 3D laparoscopic distal gastrectomy with Roux-en-Y anastomosis were allocated into Roux-en-Y group, respectively. Observation indicators: (1) surgical situations; (2) postoperative situations; (3) follow-up. Follow-up using outpatient examination and telephone interview was conducted to detect remnant gastritis and its severity, bile reflux, reflux esophagitis in the postoperative 3 months up to April 2020. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the t test. Count data were described as absolute numbers or percentages, and comparison between groups was analyzed using the chi-square test or Fisher exact probability. Comparison of ordinal data was analyzed using the Mann-Whitney U test. Results:(1) Surgical situations: 140 patients underwent totally 3D laparoscopic distal gastrectomy. The operation time, cases with volume of intraoperative blood loss <50 mL, 50 to 200 mL or >200 mL, the number of lymph node dissected were (233±39)minutes,15, 35, 4, 30±13 for the Billroth Ⅱ +Braun group , respectively, versus (240±52)minutes,25, 51, 10, 27±10 for the Roux-en-Y group, showing no significant difference between the two groups ( t=0.856， χ2=0.774， t=1.518， P>0.05). (2) Postoperative situations: cases with drainage tube, time to postoperative first flatus, cases with postoperative grade Ⅱ, Ⅲ, Ⅳ, Ⅴ complications, cases with postoperative complications, cases with postoperative severe complications, duration of postoperative hospital stay, surgery cost and total hospitalization cost of the Billroth Ⅱ+Braun group were 38, (3.5±0.8)days,4, 1, 0, 0, 5, 1, (9.0±5.0)days, (3.8±1.2)×10 4 yuan and (9.7±2.1)×10 4 yuan, respectively. The above indicators of the Roux-en-Y group were 59, (3.7±1.0)days, 9, 1, 0, 1, 11, 2, (9.0±4.0)days, (4.3±1.0)×10 4 yuan and (9.2±2.1)×10 4 yuan, respectively. There was a significant difference in the surgery cost between the two groups ( t=2.453, P<0.05), while there was no significant difference in cases with drainage tube, time to postoperative first flatus, cases with postoperative grade Ⅱ, Ⅲ, Ⅳ, Ⅴ complications, cases with postoperative complications, duration of postoperative hospital stay or total hospitalization cost between the two groups ( χ2=0.049, t=?1.339, Z=0.000, χ2=0.409, t=0.197, 1.383, P>0.05). There was also no significant difference in cases with postoperative severe complications between the two groups ( P>0.05).(3) Follow-up: 134 of 140 patients received the follow-up, including 52 cases in the Billroth Ⅱ+Braun group and 82 cases in the Roux-en-Y group. Results of follow-up within postoperative 3 months showed that the incidence rates of remnant gastritis, bile reflux, reflux esophagitis were 61.5%(32/52), 38.5%(20/52), 26.9%(14/52) for the Billroth Ⅱ+Braun group, respectively, versus 41.5%(34/82), 22.0%(18/82), 12.2%(10/82) for the Roux-en-Y group, showing significant differences between the two groups ( χ2=5.131, 4.270, 4.695, P<0.05). Cases with grade 0,Ⅰ,Ⅱ, Ⅲ, Ⅳ residual food were 42, 3, 5, 2,0 for the Billroth Ⅱ+Braun group, versus 67, 9, 1, 5,0 for the Roux-en-Y group, showing no significant difference between the two groups ( Z=?0.156, P>0.05). Cases with minimal lesion, grade A, grade B gastritis (severity of gastritis) were 6, 5, 3 for the Billroth Ⅱ+Braun group, versus 8, 2, 0 for the Roux-en-Y group, showing no significant difference between the two groups ( Z=?1.468, P>0.05). Conclusions:It is safe and feasible to operate Billroth Ⅱ+Braun or Roux-en-Y anastomosis in totally 3D laparoscopic distal gastrectomy. Billroth Ⅱ+Braun anastomosis can reduce the surgical cost. Roux-en-Y anastomosis has advantages in reducing the incidence of reflux esophagitis, bile reflux and reflux gastritis.

Renin-angiotensin system blockers and mortality in COVID-19: a territory-wide study from Hong Kong

AimsRenin-angiotensin system blockers such as angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) may increase the risk of adverse outcomes in COVID-19. In this study, the relationships between ACEI/ARB use and COVID-19 related mortality were examined. MethodsConsecutive patients diagnosed with COVID-19 by RT-PCR at the Hong Kong Hospital Authority between 1st January and 28th July 2020 were included. ResultsThis study included 2774 patients. The mortality rate of the COVID-19 positive group was 1.5% (n=42). Those who died had a higher median age (82.3[76.5-89.5] vs. 42.9[28.2-59.5] years old; P<0.0001), more likely to have baseline comorbidities of cardiovascular disease, diabetes mellitus, hypertension, and chronic kidney disease (P<0.0001). They were more frequently prescribed ACEI/ARBs at baseline, and steroids, lopinavir/ritonavir, ribavirin and hydroxychloroquine during admission (P<0.0001). They also had a higher white cell count, higher neutrophil count, lower platelet count, prolonged prothrombin time and activated partial thromboplastin time, higher D-dimer, troponin, lactate dehydrogenase, creatinine, alanine transaminase, aspartate transaminase and alkaline phosphatase (P<0.0001). Multivariate Cox regression showed that age, cardiovascular disease, renal disease, diabetes mellitus, the use of ACEIs/ARBs and diuretics, and various laboratory tests remained significant predictors of mortality. ConclusionsWe report that an association between ACEIs/ARBs with COVID-19 related mortality even after adjusting for cardiovascular and other comorbidities, as well as medication use. Patients with greater comorbidity burden and laboratory markers reflecting deranged clotting, renal and liver function, and increased tissue inflammation, and ACEI/ARB use have a higher mortality risk. Key PointsO_LIWe report that an association between ACEIs/ARBs with COVID-19 related mortality even after adjusting for cardiovascular and other comorbidities, as well as medication use. C_LIO_LIPatients with greater comorbidity burden and laboratory markers reflecting deranged clotting, renal and liver function, and increased tissue inflammation, and ACEI/ARB use have a higher mortality risk. C_LI

Identifying Sequential Complication and Mortality Patterns in Diabetes Mellitus: Comparisons of Machine Learning Methodologies

BackgroundDiabetes mellitus-related complications adversely affect the quality of life. Better risk-stratified care through mining of sequential complication patterns is needed to enable early detection and prevention. MethodsUnivariable and multivariate logistic regression was used to identify significant variables that can predict mortality. A sequence analysis method termed Prefixspan was applied to identify the most common couple, triple, quadruple, quintuple and sextuple sequential complication patterns in the directed comorbidity pathology network. A knowledge enhanced CPT+ (KCPT+) sequence prediction model is developed to predict the next possible outcome along the progression trajectories of diabetes-related complications. FindingsA total of 14,144 diabetic patients (51% males) were included. Acute myocardial infarction (AMI) without known ischaemic heart disease (IHD) (odds ratio [OR]: 2.8, 95% CI: [2.3, 3.4]), peripheral vascular disease (OR: 2.3, 95% CI: [1.9, 2.8]), dementia (OR: 2.1, 95% CI: [1.8, 2.4]), and IHD with AMI (OR: 2.4, 95% CI: [2.1, 2.6]) are the most important multivariate predictors of mortality. KCPT+ shows high accuracy in predicting mortality (F1 score 0.90, ACU 0.88), osteoporosis (F1 score 0.86, AUC 0.82), ophthalmological complications (F1 score 0.82, AUC 0.82), IHD with AMI (F1 score 0.81, AUC 0.85) and neurological complications (F1 score 0.81, AUC 0.83) with a particular prior complication sequence. InterpretationSequence analysis identifies the most common pattern characteristics of disease-related complications efficiently. The proposed sequence prediction model is accurate and enables clinicians to diagnose the next complication earlier, provide better risk-stratified care, and devise efficient treatment strategies for diabetes mellitus patients.

The impact of non-pharmaceutical interventions on the prevention and control of COVID-19 in New York City

The emergence of coronavirus disease 2019 (COVID-19) has infected more than 37 million people worldwide. The control responses varied across countries with different outcomes in terms of epidemic size and social disruption. In this study, we presented an age-specific susceptible-exposed-infected-recovery-death model that considers the unique characteristics of COVID-19 to examine the effectiveness of various non-pharmaceutical interventions (NPIs) in New York City (NYC). Numerical experiments from our model show that the control policies implemented in NYC reduced the number of infections by 72% (IQR 53-95), and the number of deceased cases by 76% (IQR 58-96) by the end of 2020, respectively. Among all the NPIs, social distancing for the entire population and the protection for the elderly in the public facilities is the most effective control measure in reducing severe infections and deceased cases. School closure policy may not work as effectively as one might expect in terms of reducing the number of deceased cases. Our simulation results provide novel insights into the city-specific implementation of NPIs with minimal social disruption considering the locations and population characteristics.

Role of asymptomatic COVID-19 cases in viral transmission: Findings from a hierarchical community contact network model

BackgroundAs part of on-going efforts to contain the COVID-19 pandemic, understanding the role of asymptomatic patients in the transmission system is essential to infection control. However, optimal approach to risk assessment and management of asymptomatic cases remains unclear. MethodsThis study involved a SEINRHD epidemic propagation model, constructed based on epidemiological characteristics of COVID-19 in China, accounting for the heterogeneity of social network. We assessed epidemic control measures for asymptomatic cases on three dimensions. Impact of asymptomatic cases on epidemic propagation was examined based on the effective reproduction number, abnormally high transmission events, and type and structure of transmission. ResultsManagement of asymptomatic cases can help flatten the infection curve. Tracking 75% of asymptomatic cases corresponds to an overall reduction in new cases by 34.3% (compared to tracking no asymptomatic cases). Regardless of population-wide measures, family transmission is higher than other types of transmission, accounting for an estimated 50% of all cases. ConclusionsAsymptomatic case tracking has significant effect on epidemic progression. When timely and strong measures are taken for symptomatic cases, the overall epidemic is not sensitive to the implementation time of the measures for asymptomatic cases.

Development of a predictive risk model for severe COVID-19 disease using population-based administrative data

BackgroundRecent studies have reported numerous significant predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk score for prompt risk stratification. The objective is to develop a simple risk score for severe COVID-19 disease using territory-wide healthcare data based on simple clinical and laboratory variables. MethodsConsecutive patients admitted to Hong Kongs public hospitals between 1st January and 22nd August 2020 diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8th September 2020. ResultsCOVID-19 testing was performed in 237493 patients and 4445 patients (median age 44.8 years old, 95% CI: [28.9, 60.8]); 50% male) were tested positive. Of these, 212 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, hypertension, stroke, diabetes mellitus, ischemic heart disease/heart failure, respiratory disease, renal disease, increases in neutrophil count, monocyte count, sodium, potassium, urea, alanine transaminase, alkaline phosphatase, high sensitive troponin-I, prothrombin time, activated partial thromboplastin time, D-dimer and C-reactive protein, as well as decreases in lymphocyte count, base excess and bicarbonate levels. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. ConclusionsA simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results.

The collective wisdom in the COVID-19 research: comparison and synthesis of epidemiological parameter estimates in preprints and peer-reviewed articles

SummaryO_ST_ABSBackgroundC_ST_ABSResearch papers related to COVID-19 have exploded. We aimed to explore the academic value of preprints through comparing with peer-reviewed publications, and synthesize the parameter estimates of the two kinds of literature. MethodWe collected papers regarding the estimation of four key epidemiological parameters of the COVID-19 in China: the basic reproduction number (R0), incubation period, infectious period, and case-fatality-rate (CFR). PubMed, Google Scholar, medRxiv, bioRxiv, arRxiv, and SSRN were searched by 20 March, 2020. Distributions of parameters and timeliness of preprints and peer-reviewed papers were compared. Further, four parameters were synthesized by bootstrap, and their validity was verified by susceptible-exposed-infectious-recovered-dead-cumulative (SEIRDC) model based on the context of China. Findings106 papers were included for analysis. The distributions of four parameters in two literature groups were close, despite that the timeliness of preprints was better. Four parameter estimates changed over time. Synthesized estimates of R0 (3{middle dot}18, 95% CI 2{middle dot}85-3{middle dot}53), incubation period (5{middle dot}44 days, 95% CI 4{middle dot}98-5{middle dot}99), infectious period (6{middle dot}25 days, 95% CI 5{middle dot}09-7{middle dot}51), and CFR (4{middle dot}51%, 95% CI 3{middle dot}41%-6{middle dot}29%) were obtained from the whole parameters space, all with p<0{middle dot}05. Their validity was evaluated by simulated cumulative cases of SEIRDC model, which matched well with the onset cases in China. InterpretationPreprints could reflect the changes of epidemic situation sensitively, and their academic value shouldnt be neglected. Synthesized results of literatures could reduce the uncertainty and be used for epidemic decision making. FundingThe National Natural Science Foundation of China and Beijing Municipal Natural Science Foundation. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSSince its outbreak, scientific articles about the COVID-19 have greatly surged, with a significant portion as non-peer-reviewed preprints. Although preprints captured great attention, the credibility of preprints was widely debated. We searched PubMed and Google on March 20, 2020, for publications that discussed the preprints during the COVID-19 pandemic, using the terms ("preprints" AND "COVID-19"). We identified 12 papers and news, and found that scientists were skeptical of preprints mainly because rigorous peer review is absent and thus the conclusions of preprints may not be reliable. However, scientists opinions could have been biased towards limited data, and there is few knowledges about the validity of the results reported in the preprints. Further, to examine how scientists utilize results of preprints, taking the epidemiological parameter estimation as the objects, we searched reviews on Google using the terms ("epidemiology" AND ("meta-analysis" OR "reviews") AND "COVID-19") on May 23, 2020. Nine papers were identified. We found that existing meta-analysis and reviews included few preprints. This may be due to the fact that the quality of preprints was not recognized, and thus their academic value was underestimated. Overall, the validity of the results as reported in the preprints should be further examined and the potential of synthesizing preprints with formally published papers should be explored. Added value of this studyOur study adds value in four main ways. First, we collected preprints and peer-reviewed papers on estimations of the four most important epidemiological parameters (the basic reproduction number, incubation period, infectious period, and case-fatality-rate) for the COVID-19 outbreak in China. 106 papers were included and available data were extracted. Second, we quantitatively compared the differences and timeliness between preprints and peer-reviewed publications in the estimation of the four parameters, and found that the validity of the preprints estimations was largely consistent with that of the peer-reviewed group. Third, we synthesized the estimations of the two groups of literatures using bootstrap method, and found that the values of infectious period and case-fatality-rate decreased over time, indicating that the synthesized results timely reflected the changing trend of the COVID-19 in China. Finally, the practicability of the synthesized parameter estimations was verified by the data of confirmed cases in China. The cumulative infection curve simulated using synthesized parameters fitted the real data well. Implications of all the available evidenceResults of our study indicate that the validity of the COVID-19 parameter estimations of the preprints is on par with that of peer-reviewed publications, and the preprints are relatively timelier. Further, the synthesized parameters of the two literature groups can effectively reduce the uncertainty and capture the patterns of epidemics. These results provide data-driven insights into the academic value of preprints, which have been arguably underestimated. The scientific community should actively capitalize the collective wisdom generated by the huge amount of preprints, particularly during the emerging infectious diseases like the COVID-19.

Identifying main and interaction effects of risk factors to predict intensive care admission in patients hospitalized with COVID-19: a retrospective cohort study in Hong Kong

BackgroundThe coronavirus disease 2019 (COVID-19) has become a pandemic, placing significant burdens on the healthcare systems. In this study, we tested the hypothesis that a machine learning approach incorporating hidden nonlinear interactions can improve prediction for Intensive care unit (ICU) admission. MethodsConsecutive patients admitted to public hospitals between 1st January and 24th May 2020 in Hong Kong with COVID-19 diagnosed by RT-PCR were included. The primary endpoint was ICU admission. ResultsThis study included 1043 patients (median age 35 (IQR: 32-37; 54% male). Nineteen patients were admitted to ICU (median hospital length of stay (LOS): 30 days, median ICU LOS: 16 days). ICU patients were more likely to be prescribed angiotensin converting enzyme inhibitors/angiotensin receptor blockers, anti-retroviral drugs lopinavir/ritonavir and remdesivir, ribavirin, steroids, interferon-beta and hydroxychloroquine. Significant predictors of ICU admission were older age, male sex, prior coronary artery disease, respiratory diseases, diabetes, hypertension and chronic kidney disease, and activated partial thromboplastin time, red cell count, white cell count, albumin and serum sodium. A tree-based machine learning model identified most informative characteristics and hidden interactions that can predict ICU admission. These were: low red cells with 1) male, 2) older age, 3) low albumin, 4) low sodium or 5) prolonged APTT. A five-fold cross validation confirms superior performance of this model over baseline models including XGBoost, LightGBM, random forests, and multivariate logistic regression. ConclusionsA machine learning model including baseline risk factors and their hidden interactions can accurately predict ICU admission in COVID-19.

Healthcare-resource-adjusted vulnerabilities towards the 2019-nCoV epidemic across China

We integrate the human movement and healthcare resource data to identify cities with high vulnerability towards the 2019-nCoV epidemic with respect to available health resources. The results inform public health responses in multiple ways.

Incorporating Human Movement Data to Improve Epidemiological Estimates for 2019-nCoV

Estimating the key epidemiological features of the novel coronavirus (2019-nCoV) epidemic proves to be challenging, given incompleteness and delays in early data reporting, in particular, the severe under-reporting bias in the epicenter, Wuhan, Hubei Province, China. As a result, the current literature reports widely varying estimates. We developed an alternative geo-stratified debiasing estimation framework by incorporating human mobility with case reporting data in three stratified zones, i.e., Wuhan, Hubei Province excluding Wuhan, and mainland China excluding Hubei. We estimated the latent infection ratio to be around 0.12% (18,556 people) and the basic reproduction number to be 3.24 in Wuhan before the citys lockdown on January 23, 2020. The findings based on this debiasing framework have important implications to prioritization of control and prevention efforts. One Sentence SummaryA geo-stratified debiasing approach incorporating human movement data was developed to improve modeling of the 2019-nCoV epidemic.

Estimating the effective reproduction number of the 2019-nCoV in China

We estimate the effective reproduction number for 2019-nCoV based on the daily reported cases from China CDC. The results indicate that 2019-nCoV has a higher effective reproduction number than SARS with a comparable fatality rate. Article Summary LineThis modeling study indicates that 2019-nCoV has a higher effective reproduction number than SARS with a comparable fatality rate.

Application of laparoscopy in gastrointestinal abdominal emergency operation for patients over 65 years old / 中华胃肠外科杂志

<p><b>OBJECTIVE</b>To explore the clinical application of laparoscopy in gastrointestinal abdominal emergency operation for patients over 65 years old.</p><p><b>METHODS</b>Clinical data of 138 cases (age>65 years) with acute abdomen undergoing laparoscopic surgery from January 2006 to June 2014 were analyzed retrospectively. Data of 170 cases treated by laparotomy during the same period were enrolled as controls.</p><p><b>RESULTS</b>The laparoscopy group and the laparotomy group showed statistically significant differences in blood loss [(107.1±47.7) ml vs. (163.6±106.5) ml, P=0.000], postoperative complications rate [2.9%(4/138) vs. 12.9%(22/170), P=0.022], hospital stay [(10.5±7.5) d vs. (16.5±9.9) d, P=0.044], postoperative ambulation time [(25.6±7.7) h vs. (33.2±5.6) h, P=0.020], and recovery time of postoperative gastrointestinal function [(36.9±9.1) h vs. (49.3±10.6) h, P=0.031]. Patients with acute appendicitis, upper digestive tract perforation and bowel obstruction in the laparoscopy group were superior to those in the laparotomy group in hospital stay, postoperative ambulation time, recovery time of postoperative gastrointestinal function and intraoperative blood loss(all P<0.01), while no significant differences in colon perforation and mesentery diseases were found in hospital stay, intraoperative blood loss and recovery time of postoperative gastrointestinal function between the two groups (all P>0.05).</p><p><b>CONCLUSIONS</b>Compared with laparotomy, the laparoscopy offers the advantages of less trauma, faster recovery, shorter hospital stay, and lower postoperative complications rate for patients over 65 years with acute abdomen.</p>