COVID-19 in the Tibet, China, the roof of the world: a comparative analysis of high-altitude residents and newcomers.

BACKGROUND: After a 920-day hiatus, COVID-19 resurged in the Tibet Autonomous Region of China in August 2022. This study compares the characteristics of COVID-19 between high-altitude residents and newcomers, as well as between newcomers and lowlanders. METHODS: This multi-center cohort study conducted at the Third People's Hospital of Tibet Autonomous Region and Beijing University Shenzhen Hospital, included 520 high-altitude resident patients, 53 high-altitude newcomer patients, and 265 lowlander patients infected with the Omicron variant. Initially, we documented epidemiological, clinical, and treatment data across varying residency at admission. We compared the severity of COVID-19 and various laboratory indicators, including hemoglobin concentration and SpO2%, over a 14-day period from the date of the first positive nucleic acid test, as well as the differences in treatment methods and disease outcomes between highlanders and high-altitude newcomers. We also compared several characteristics of COVID-19 between high-altitude newcomers and lowlanders. Univariate analysis, multivariable logistic regression, and the generalized linear mixed model were utilized for the analysis. RESULTS: No fatalities were observed. The study found no significant differences in COVID-19 severity or in the physiological measures of hemoglobin concentration and SpO2% between high-altitude and lowland residents. Similarly, there were no statistically significant differences in the values or trends of hemoglobin and SpO2% between high-altitude residents and newcomers throughout the 14-day observation period. However, compared to age- and sex-matched lowlander patients (1:5 ratio), high-altitude newcomers exhibited higher heart rates, respiratory rates, and average hemoglobin concentrations, along with lower platelet counts. There were no significant differences in hospital stays between the two groups. CONCLUSIONS: High-altitude residents and newcomer patients exhibit clinical similarities. However, the clinical characteristics of high-altitude newcomers and lowlander patients differ due to the impact of the high-altitude environment. These results highlight potential considerations for public health strategies in high-altitude regions such as Tibet.

Association of emergency room visits for respiratory diseases with sources of ambient PM2.5.

Previous studies have reported associations of short-term exposure to different sources of ambient fine particulate matter (PM2.5) and increased mortality or hospitalizations for respiratory diseases. Few studies, however, have focused on the short-term effects of source-specific PM2.5 on emergency room visits (ERVs) of respiratory diseases. Source apportionment for PM2.5 was performed with Positive Matrix Factorization (PMF) and generalized additive model was applied to estimate associations between source-specific PM2.5 and respiratory disease ERVs. The association of PM2.5 and total respiratory ERVs was found on lag4 (RR = 1.011, 95%CI: 1.002, 1.020) per interquartile range (76 µg/m3) increase. We found PM2.5 to be significantly associated with asthma, bronchitis and chronic obstructive pulmonary disease (COPD) ERVs, with the strongest effects on lag5 (RR = 1.072, 95%CI: 1.024, 1.119), lag4 (RR = 1.104, 95%CI: 1.032, 1.176) and lag3 (RR = 1.091, 95%CI: 1.047, 1.135), respectively. The estimated effects of PM2.5 changed little after adjusting for different air pollutants. Six primary PM2.5 sources were identified using PMF analysis, including dust/soil (6.7%), industry emission (4.5%), secondary aerosols (30.3%), metal processing (3.2%), coal combustion (37.5%) and traffic-related source (17.8%). Some of the sources were identified to have effects on ERVs of total respiratory diseases (dust/soil, secondary aerosols, metal processing, coal combustion and traffic-related source), bronchitis ERVs (dust/soil) and COPD ERVs (traffic-related source, industry emission and secondary aerosols). Different sources of PM2.5 contribute to increased risk of respiratory ERVs to different extents, which may provide potential implications for the decision making of air quality related policies, rational emission control and public health welfare.

Associations of multiple risk factors with prenatal depression and anxiety: Evidence from the Tianjin Birth Cohort (TJBC) study.

BACKGROUND: Antenatal mental disorders are associated with maternal and fetal adverse events. Previous studies have been focused on the postpartum period, rather than pregnancy, yet the association of risk factors with prenatal depression and anxiety through pregnancy has been rarely reported. This study aimed to identify the risk factors of prenatal depression and anxiety, and access their potential roles in developing mental disorders during pregnancy. METHODS: This is a prospective study in 6470 participants from the Tianjin Birth Cohort in China (TJBC). The degree of prenatal depression and anxiety was evaluated using a questionnaire of Self-Rating Depression scale (SDS) and Self-Rating Anxiety Scale (SAS), which was given to pregnant women at 15-27 (Stage-2), and 28-41 (Stage-3) gestational weeks. The questionnaire also collected demographic, personal, and lifestyle information. The association of different factors with SDS/SAS score was examined by logistic regression analysis. RESULTS: We observed an overall depression rate of 12.4 % and an overall anxiety rate of 7.7 % during pregnancy in the TJBC. In the Stage-2, the depression rate was 14.5 % and the anxiety rate was 9.5 %. In the Stage-3, the depression rate dropped to 9.7 % while the anxiety rate dropped to 5.3 %. With univariate analysis, we found that age, education, social support, marriage satisfaction, secondhand smoke (SHS), sleeping time and stress were common factors of prenatal mental health. Working status, family income, gravidity, smoking, electronic using, recreational activities were associated with depression risk, whereas BMI, disease history, changing eating habits, and feeding animal were associated with anxiety risk. Using logistic regression, we found that low education level, low social support, low marriage satisfaction, thyroid disfunction, Stage-2(second trimester), and stress were related to prenatal mental health. CONCLUSION: The prevalence anxiety and depression in Tianjin is normal as national level. Age appropriateness, a good education level, sufficient social support, marital satisfaction, normal thyroid function, and absence of stress are associated with relieving depression and anxiety during gestation. However, due to individual difference, expectant mothers should seek professional support and guidance to address their mental health needs during gestation.

Longitudinal integrative cell-free DNA analysis in gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) presents varied manifestations throughout pregnancy and poses a complex clinical challenge. High-depth cell-free DNA (cfDNA) sequencing analysis holds promise in advancing our understanding of GDM pathogenesis and prediction. In 299 women with GDM and 299 matched healthy pregnant women, distinct cfDNA fragment characteristics associated with GDM are identified throughout pregnancy. Integrating cfDNA profiles with lipidomic and single-cell transcriptomic data elucidates functional changes linked to altered lipid metabolism processes in GDM. Transcription start site (TSS) scores in 50 feature genes are used as the cfDNA signature to distinguish GDM cases from controls effectively. Notably, differential coverage of the islet acinar marker gene PRSS1 emerges as a valuable biomarker for GDM. A specialized neural network model is developed, predicting GDM occurrence and validated across two independent cohorts. This research underscores the high-depth cfDNA early prediction and characterization of GDM, offering insights into its molecular underpinnings and potential clinical applications.

Toxic epidermal necrolysis with systemic lupus erythematosus: case report and review of the literature.

Toxic epidermal necrolysis (TEN) and Stevens-Johnson syndrome (SJS) are potentially fatal mucocutaneous diseases characterized by extensive necrosis and exfoliation of the epidermis. TEN and SJS are most often caused by various kinds of drugs. Other risk factors for SJS/TEN include pneumonia infection, HIV infection, genetic factors, underlying immune diseases, and tumors. SJS and TEN were first identified in 1922, but at present, a widely recognized view is that SJS and TEN represent phases in the continuous progress of the same disease. SJS/TEN has a very high mortality, but is rare, and cases of SJS/TEN combined with systemic lupus erythematosus (SLE) are even less common. Occasionally, acute cutaneous manifestations of SLE and SJS/TEN can be phenotypically similar, both causing extensive epidermal necrosis. In this paper, we present a recent case of a 32-year-old female SLE patient with a drug-induced (the health product, astaxanthin) TEN/SJS. To provide context to this case, we have reviewed relevant case studies published in English, accessed via PubMed databases. The search covers all published case studies from 1988 to 2019. We collected a total of 30 cases in the literature, and analyzed their characteristics from the aspects of gender, suspicious medication history, and treatment in order to expand clinicians' approach to diagnosis and treatment.

Correlation between the warning symptoms and prognosis of cardiac arrest.

BACKGROUND: A low survival rate in patients with cardiac arrest is associated with failure to recognize the condition in its initial stage. Therefore, recognizing the warning symptoms of cardiac arrest in the early stage may play an important role in survival. AIM: To investigate the warning symptoms of cardiac arrest and to determine the correlation between the symptoms and outcomes. METHODS: We included all adult patients with all-cause cardiac arrest who visited Peking University Third Hospital or Beijing Friendship Hospital between January 2012 and December 2014. Data on population, symptoms, resuscitation parameters, and outcomes were analysed. RESULTS: Of the 1021 patients in the study, 65.9% had symptoms that presented before cardiac arrest, 25.2% achieved restoration of spontaneous circulation (ROSC), and 7.2% survived to discharge. The patients with symptoms had higher rates of an initial shockable rhythm (12.2% vs 7.5%, P = 0.020), ROSC (29.1% vs 17.5%, P = 0.001) and survival (9.2% vs 2.6%, P = 0.001) than patients without symptoms. Compared with the out-of-hospital cardiac arrest (OHCA) without symptoms subgroup, the OHCA with symptoms subgroup had a higher rate of calls before arrest (81.6% vs 0.0%, P < 0.001), health care provider-witnessed arrest (13.0% vs 1.4%, P = 0.001) and bystander cardiopulmonary resuscitation (15.5% vs 4.9%, P = 0.002); a shorter no flow time (11.7% vs 2.8%, P = 0.002); and a higher ROSC rate (23.8% vs 13.2%, P = 0.011). Compared to the in-hospital cardiac arrest (IHCA) without symptoms subgroup, the IHCA with symptoms subgroup had a higher mean age (66.2 ± 15.2 vs 62.5 ± 16.3 years, P = 0.005), ROSC (32.0% vs 20.6%, P = 0.003), and survival rates (10.6% vs 2.5%, P < 0.001). The top five warning symptoms were dyspnea (48.7%), chest pain (18.3%), unconsciousness (15.2%), paralysis (4.3%), and vomiting (4.0%). Chest pain (20.9% vs 12.7%, P = 0.011), cardiac etiology (44.3% vs 1.5%, P < 0.001) and survival (33.9% vs 16.7%, P = 0.001) were more common in males, whereas dyspnea (54.9% vs 45.9%, P = 0.029) and a non-cardiac etiology (53.3% vs 41.7%, P = 0.003) were more common in females. CONCLUSION: Most patients had warning symptoms before cardiac arrest. Dyspnea, chest pain, and unconsciousness were the most common symptoms. Immediately recognizing these symptoms and activating the emergency medical system prevents resuscitation delay and improves the survival rate of OHCA patients in China.

Early Prediction Model of Acute Aortic Syndrome Mortality in Emergency Departments.

Purpose: Acute aortic syndrome is a constellation of life-threatening medical conditions for which rapid assessment and targeted intervention are important for the prognosis of patients who are at high risk of in-hospital death. The current study aims to develop and externally validate an early prediction mortality model that can be used to identify high-risk patients with acute aortic syndrome in the emergency department. Patients and Methods: This retrospective multi-center observational study enrolled 1088 patients with acute aortic syndrome admitted to the emergency departments of two hospitals in China between January 2017 and March 2021 for model development. A total of 210 patients with acute aortic syndrome admitted to the emergency departments of Peking University Third Hospital between January 2007 and December 2021 was enrolled for model validation. Demographics and clinical factors were collected at the time of emergency department admission. The predictive variables were determined by referring to the results of previous studies and the baseline analysis of this study. The study's endpoint was in-hospital death. To assess internal validity, we used a fivefold cross-validation method. Model performance was validated internally and externally by evaluating model discrimination using the area under the receiver-operating characteristic curve (AUC). A nomogram was developed based on the binary regression results. Results: In the development cohort, 1088 patients with acute aortic syndromes were included, and 88 (8.1%) patients died during hospitalization. In the validation cohort, 210 patients were included, and 20 (9.5%) patients died during hospitalization. The final model included the following variables: digestive system symptoms (OR=2.25; P=0.024), any pulse deficit (OR=7.78; P<0.001), creatinine (µmol/L)(OR=1.00; P=0.018), lesion extension to iliac vessels (OR=4.49; P<0.001), pericardial effusion (OR=2.67; P=0.008), and Stanford type A (OR=10.46; P<0.001). The model's AUC was 0.838 (95% CI 0.784-0.892) in the development cohort and 0.821 (95% CI 0.750-0.891) in the validation cohort, and the Hosmer-Lemeshow test showed p=0.597. The fivefold cross-validation demonstrated a mean accuracy of 0.94, a mean precision of 0.67, and a mean recall of 0.13. Conclusion: This risk prediction tool uses simple variables to provide robust prediction of the risk of in-hospital death from acute aortic syndrome and validated well in an independent cohort. The tool can help emergency clinicians quickly identify high-risk acute aortic syndrome patients, although further studies are needed for verifying the prospective data and the results of our study.

A Retrospective Study: Quick Scoring of Symptoms to Estimate the Risk of Cardiac Arrest in the Emergency Department.

Purpose: At present, not enough is known about the symptoms before cardiac arrest. The purpose of this study is to describe the precursor symptoms of cardiac arrest, focusing on the relationship between symptoms and cardiac arrest, and to establish a quick scoring model of symptoms for predicting cardiac arrest. Patients and Methods. A retrospective case-control study was carried out on cardiac arrest patients who visited the emergency department of Peking University Third Hospital from January 2018 to June 2019. Symptoms that occurred or were obviously aggravated within the 14 days before CA were defined as warning symptoms. Results: More than half the cardiac arrest patients experienced warning symptoms within 14 days before cardiac arrest. Dyspnea (p < 0.001) was found to be associated with cardiac arrest; syncope and cold sweat are other symptoms that may have particular clinical significance. Gender (p < 0.001), age (p < 0.001), history of heart failure (p=0.006), chronic kidney disease (p=0.011), and hyperlipidemia (p=0.004) were other factors contributing to our model. Conclusions: Warning symptoms during the 14 days prior to cardiac arrest are common for CA patients. The Quick Scoring Model for Cardiac Arrest (QSM-CA) was developed to help emergency physicians and emergency medical services (EMS) personnel quickly identify patients with a high risk of cardiac arrest.

Case Report: An Unusual First Manifestation of a Pheochromocytoma.

We present a 30-year old male presented with hemoptysis as a first manifestation and pulmonary CT scan characteristic of diffuse alveolar hemorrhage. Further abdominal examination discovered a left adrenal mass. Elevated catecholamine and metanephrine levels and subsequent adrenalectomy confirmed the diagnosis of pheochromocytoma. Symptoms of pheochromocytoma are highly variable, which could potentially delay the diagnosis. With careful investigation, emergency medicine practitioners need to be aware of the cunning and changeful manifestations in pheochromocytoma.

Optimal Time of Collapse to Return of Spontaneous Circulation to Apply Targeted Temperature Management for Cardiac Arrest: A Bayesian Network Meta-Analysis.

Background: Both the American Heart Association (AHA) and European Resuscitation Council (ERC) have strongly recommended targeted temperature management (TTM) for patients who remain in coma after return of spontaneous circulation (ROSC). However, the role of TTM, especially hypothermia, in cardiac arrest patients after TTM2 trials has become much uncertain. Methods: We searched four online databases (PubMed, Embase, CENTRAL, and Web of Science) and conducted a Bayesian network meta-analysis. Based on the time of collapse to ROSC and whether the patient received TTM or not, we divided this analysis into eight groups (<20 min + TTM, <20 min, 20-39 min + TTM, 20-39 min, 40-59 min + TTM, 40-59 min, ≥60 min + TTM and ≥60 min) to compare their 30-day and at-discharge survival and neurologic outcomes. Results: From an initial search of 3,023 articles, a total of 9,005 patients from 42 trials were eligible and were included in this network meta-analysis. Compared with other groups, patients in the <20 min + TTM group were more likely to have better survival and good neurologic outcomes (probability = 46.1 and 52.5%, respectively). In comparing the same time groups with and without TTM, only the survival and neurologic outcome of the 20-39 min + TTM group was significantly better than that of the 20-39 min group [odds ratio = 1.41, 95% confidence interval (1.04-1.91); OR = 1.46, 95% CI (1.07-2.00) respectively]. Applying TTM with <20 min or more than 40 min of collapse to ROSC did not improve survival or neurologic outcome [ <20 min vs. <20 min + TTM: OR = 1.02, 95% CI (0.61-1.71)/OR = 1.03, 95% CI (0.61-1.75); 40-59 min vs. 40-59 min + TTM: OR = 1.50, 95% CI (0.97-2.32)/OR = 1.40, 95% CI (0.81-2.44); ≧60 min vs. ≧60 min + TTM: OR = 2.09, 95% CI (0.70-6.24)/OR = 4.14, 95% CI (0.91-18.74), respectively]. Both survival and good neurologic outcome were closely related to the time from collapse to ROSC. Conclusion: Survival and good neurologic outcome are closely associated with the time of collapse to ROSC. These findings supported that 20-40 min of collapse to ROSC should be a more suitable indication for TTM for cardiac arrest patients. Moreover, the future trials should pay more attention to these patients who suffer from moderate injury. Systematic Review Registration: [https://inplasy.com/?s=202180027], identifier [INPLASY202180027].

Using machine learning tools to predict outcomes for emergency department intensive care unit patients.

The number of critically ill patients has increased globally along with the rise in emergency visits. Mortality prediction for critical patients is vital for emergency care, which affects the distribution of emergency resources. Traditional scoring systems are designed for all emergency patients using a classic mathematical method, but risk factors in critically ill patients have complex interactions, so traditional scoring cannot as readily apply to them. As an accurate model for predicting the mortality of emergency department critically ill patients is lacking, this study's objective was to develop a scoring system using machine learning optimized for the unique case of critical patients in emergency departments. We conducted a retrospective cohort study in a tertiary medical center in Beijing, China. Patients over 16 years old were included if they were alive when they entered the emergency department intensive care unit system from February 2015 and December 2015. Mortality up to 7 days after admission into the emergency department was considered as the primary outcome, and 1624 cases were included to derive the models. Prospective factors included previous diseases, physiologic parameters, and laboratory results. Several machine learning tools were built for 7-day mortality using these factors, for which their predictive accuracy (sensitivity and specificity) was evaluated by area under the curve (AUC). The AUCs were 0.794, 0.840, 0.849 and 0.822 respectively, for the SVM, GBDT, XGBoost and logistic regression model. In comparison with the SAPS 3 model (AUC = 0.826), the discriminatory capability of the newer machine learning methods, XGBoost in particular, is demonstrated to be more reliable for predicting outcomes for emergency department intensive care unit patients.

Serial disseminated intravascular coagulation score with neuron specific enolase predicts the mortality of cardiac arrest-a pilot study.

BACKGROUND: Prognosis in cardiac arrest (CA) patients has been challenging. We sought to investigate prognostic value combining serial disseminated intravascular coagulation (DIC) score and neuron-specific enolase (NSE) in out-of-hospital cardiac arrest (OHCA) patients. METHODS: Sixty-one consecutive patients successfully resuscitated after CA were included in the analysis. DIC score and NSE levels were serially analyzed after return of spontaneous circulation (ROSC). The outcome measure was death before hospital discharge. Prognostication performance was assessed as the area under the receiver-operating characteristics curve (AUC). Hosmer-Lemeshow test was used for internal validation of predictive models. Calibration curves were drawn to visualize the results of tests. RESULTS: The NSE levels continued to increase in the first 72 h in non-survivors. In survivors, the NSE levels decreased after 48 h. Both DIC score at 48 h and NSE level at 48 h were good predictors of outcome. The AUC for predictive mortality in OHCA patients was 0.869 (95% CI, 0.781-0.956) for DIC score at 48 h combining NSE at 24 h, 0.878 (95% CI, 0.791-0.965) for DIC score at 48 h combining NSE at 48 h and 0.882 (95% CI, 0.792-0.972) for DIC score at 48 h combining NSE at 72 h, respectively. Significance of Hosmer-Lemeshow test was 0.488, 0.324, 0.011 for each combination. CONCLUSIONS: Serial DIC score combined with measurement of NSE levels is a useful and accessible tool for prognostication following OHCA.

Distribution and drug resistance of pathogenic bacteria in emergency patients.

BACKGROUND: Antibiotic resistance has become a global threat for human health, calling for rational use of antibiotics. AIM: To analyze the distribution and drug resistance of the bacteria, providing the prerequisite for use of antibiotics in emergency patients. METHODS: A total of 2048 emergency patients from 2013 to 2017 were enrolled. Their clinical examination specimens were collected, followed by isolation of bacteria. The bacterial identification and drug susceptibility testing were carried out. RESULTS: A total of 3387 pathogens were isolated. The top six pathogens were Acinetobacter baumannii (660 strains), Staphylococcus aureus (436 strains), Klebsiella pneumoniae (347 strains), Pseudomonas aeruginosa (338 strains), Escherichia coli (237 strains), and Candida albicans (207 strains). The isolation rates of these pathogens decreased year by year except Klebsiella pneumoniae, which increased from 7.1% to 12.1%. Acinetobacter baumannii is a widely-resistant strain, with multiple resistances to imipenem, ciprofloxacin, minocycline and tigecycline. The Staphylococcus aureus had high resistance rates to levofloxacin, penicillin G, and tetracycline. But the susceptibility of it to vancomycin and tigecycline were 100%. Klebsiella pneumoniae had high resistance rates to imipenem, cefoperazone/sulbactam, amikacin, and ciprofloxacin, with the lowest resistance rate to tigecycline. The resistance rates of Pseudomonas aeruginosa to cefoperazone/sulbactam and imipenem were higher, with the resistance rate to amikacin below 10%. Besides, Escherichia coli had high resistance rates to ciprofloxacin and cefoperazone/sulbactam and low resistance rates to imipenem, amikacin, and tigecycline. CONCLUSION: The pathogenic bacteria isolated from the emergency patients were mainly Acinetobacter baumannii, Staphylococcus aureus, Klebsiella pneumoniae, Pseudomonas aeruginosa, Escherichia coli, and Candida albicans. The detection rates of drug-resistant bacteria were high, with different bacteria having multiple drug resistances to commonly used antimicrobial agents, guiding the rational use of drugs and reducing the production of multidrug-resistant bacteria.

Performance of Simplified Acute Physiology Score 3 In Predicting Hospital Mortality In Emergency Intensive Care Unit.

BACKGROUND: Since the 1980s, severity of illness scoring systems has gained increasing popularity in Intensive Care Units (ICUs). Physicians used them for predicting mortality and assessing illness severity in clinical trials. The objective of this study was to assess the performance of Simplified Acute Physiology Score 3 (SAPS 3) and its customized equation for Australasia (Australasia SAPS 3, SAPS 3 [AUS]) in predicting clinical prognosis and hospital mortality in emergency ICU (EICU). METHODS: A retrospective analysis of the EICU including 463 patients was conducted between January 2013 and December 2015 in the EICU of Peking University Third Hospital. The worst physiological data of enrolled patients were collected within 24 h after admission to calculate SAPS 3 score and predicted mortality by regression equation. Discrimination between survivals and deaths was assessed by the area under the receiver operator characteristic curve (AUC). Calibration was evaluated by Hosmer-Lemeshow goodness-of-fit test through calculating the ratio of observed-to-expected numbers of deaths which is known as the standardized mortality ratio (SMR). RESULTS: A total of 463 patients were enrolled in the study, and the observed hospital mortality was 26.1% (121/463). The patients enrolled were divided into survivors and nonsurvivors. Age, SAPS 3 score, Acute Physiology and Chronic Health Evaluation Score II (APACHE II), and predicted mortality were significantly higher in nonsurvivors than survivors (P < 0.05 or P < 0.01). The AUC (95% confidence intervals [CI s]) for SAPS 3 score was 0.836 (0.796-0.876). The maximum of Youden's index, cutoff, sensitivity, and specificity of SAPS 3 score were 0.526%, 70.5 points, 66.9%, and 85.7%, respectively. The Hosmer-Lemeshow goodness-of-fit test for SAPS 3 demonstrated a Chi-square test score of 10.25, P = 0.33, SMR (95% CI) = 0.63 (0.52-0.76). The Hosmer-Lemeshow goodness-of-fit test for SAPS 3 (AUS) demonstrated a Chi-square test score of 9.55, P = 0.38, SMR (95% CI) = 0.68 (0.57-0.81). Univariate and multivariate analyses were conducted for biochemical variables that were probably correlated to prognosis. Eventually, blood urea nitrogen (BUN), albumin,lactate and free triiodothyronine (FT3) were selected as independent risk factors for predicting prognosis. CONCLUSIONS: The SAPS 3 score system exhibited satisfactory performance even superior to APACHE II in discrimination. In predicting hospital mortality, SAPS 3 did not exhibit good calibration and overestimated hospital mortality, which demonstrated that SAPS 3 needs improvement in the future.