Aftab Ahmad

Background: Ischemic stroke incidence in young adults is increasing globally, with return to work (RTW) a key rehabilitation goal. We aimed to assess the overall proportions of and factors associated with RTW following young adult ischemic stroke. Methods and Results: We performed a retrospective cohort study of all patients with ischemic stroke aged 18 to 50 years at 2 tertiary hospitals in Singapore from 2020 to 2022. We evaluated associations between patient characteristics and 3‐month RTW status, augmenting these findings with a systematic review and meta‐analysis of PubMed, Embase, Scopus, and Cochrane databases from January 2000 to November 2023. We pooled proportions for RTW and functional recovery (defined as a 90‐day modified Rankin Scale score of 0–2) and meta‐analyzed associations between patient characteristics and RTW using random‐effects models. In this multicenter cohort, 68.8% (249/362) of young patients with ischemic stroke returned to work, while 87.8% (318/362) achieved functional recovery. Multivariable logistic regression showed that patients with large‐artery atherosclerosis pathogenesis, diabetes, higher admission National Institutes of Health Stroke Scale scores, and higher 90‐day modified Rankin Scale had significantly lower odds of RTW. The systematic review and meta‐analyses of 1914 patients across 6 cohort studies identified significantly lower odds of RTW in patients with large‐artery atherosclerosis, diabetes, and admission National Institutes of Health Stroke Scale SCORE >15. The pooled proportion of RTW was 63.2% (984/1574 [95% CI, 56.0–69.9]) and functional recovery 84.7% (719/846 [95% CI, 81.1–87.8]). Conclusions: Patients with large‐artery atherosclerosis, diabetes, and higher admission National Institutes of Health Stroke Scale score at baseline are less likely to RTW. While lower 90‐day modified Rankin Scale is significantly associated with RTW, many patients achieving functional recovery do not RTW. Well‐designed cohort studies are warranted to explore this disparity.

BACKGROUND: Cardiocerebral infarction (CCI), which is concomitant with acute myocardial infarction (AMI) and acute ischemic stroke (AIS), is a rare but severe presentation. However, there are few data on CCI, and the treatment options are uncertain. We investigated the characteristics and outcomes of CCI compared with AMI or AIS alone. METHODS: We performed a retrospective cohort study of 120 531 patients with AMI and AIS from the national stroke and AMI registries in Singapore. Patients were categorized into AMI only, AIS only, synchronous CCI (same-day), and metachronous CCI (within 1 week). The primary outcome was all-cause mortality, and the secondary outcome was cardiovascular mortality. The mortality risks were compared using Cox regression. Multivariable models were adjusted for baseline demographics, clinical variables, and treatment for AMI or AIS. RESULTS: Of 127 919 patients identified, 120 531 (94.2%) were included; 74 219 (61.6%) patients had AMI only, 44 721 (37.1%) had AIS only, 625 (0.5%) had synchronous CCI, and 966 (0.8%) had metachronous CCI. The mean age was 67.7 (SD, 14.0) years. Synchronous and metachronous CCI had a higher risk of 30-day mortality (synchronous: adjusted HR [aHR], 2.41 [95% CI, 1.77–3.28]; metachronous: aHR, 2.80 [95% CI, 2.11–3.73]) than AMI only and AIS only (synchronous: aHR, 2.90 [95% CI, 1.87–4.51]; metachronous: aHR, 4.36 [95% CI, 3.03–6.27]). The risk of cardiovascular mortality was higher in synchronous and metachronous CCI than AMI (synchronous: aHR, 3.03 [95% CI, 2.15–4.28]; metachronous: aHR, 3.41 [95% CI, 2.50–4.65]) or AIS only (synchronous: aHR, 2.58 [95% CI, 1.52–4.36]; metachronous: aHR, 4.52 [95% CI, 2.95–6.92]). In synchronous CCI, AMI was less likely to be managed with PCI and secondary prevention medications ( P <0.001) compared with AMI only. CONCLUSIONS: Synchronous CCI occurred in 1 in 200 cases of AIS and AMI. Synchronous and metachronous CCI had higher mortality than AMI or AIS alone.

Background: As the COVID-19 pandemic evolves, challenges in frontline work continue to impose a significant psychological impact on nurses. However, there is a lack of data on how nurses fared compared to other health care workers in the Asia-Pacific region. Objective: This study aims to investigate (1) the psychological outcome characteristics of nurses in different Asia-Pacific countries and (2) psychological differences between nurses, doctors, and nonmedical health care workers. Methods: Exploratory data analysis and visualization were conducted on the data collected through surveys. A machine learning modeling approach was adopted to further discern the key psychological characteristics differentiating nurses from other health care workers. Decision tree–based machine learning models (Light Gradient Boosting Machine, GradientBoost, and RandomForest) were built to predict whether a set of psychological distress characteristics (ie, depression, anxiety, stress, intrusion, avoidance, and hyperarousal) belong to a nurse. Shapley Additive Explanation (SHAP) values were extracted to identify the prominent characteristics of each of these models. The common prominent characteristic among these models is akin to the most distinctive psychological characteristic that differentiates nurses from other health care workers. Results: Nurses had relatively higher percentages of having normal or unchanged psychological distress symptoms relative to other health care workers (n=233-260 [86.0%-95.9%] vs n=187-199 [74.8%-91.7%]). Among those without psychological symptoms, nurses constituted a higher proportion than doctors and nonmedical health care workers (n=194 [40.2%], n=142 [29.5%], and n=146 [30.3%], respectively). Nurses in Vietnam showed the highest level of depression, stress, intrusion, avoidance, and hyperarousal symptoms compared to those in Singapore, Malaysia, and Indonesia. Nurses in Singapore had the highest level of anxiety. In addition, nurses had the lowest level of stress, which is the most distinctive psychological outcome characteristic derived from machine learning models, compared to other health care workers. Data for India were excluded from the analysis due to the differing psychological response pattern observed in nurses in India. A large number of female nurses emigrating from South India could not have psychologically coped well without the support from family members while living alone in other states. Conclusions: Nurses were least psychologically affected compared to doctors and other health care workers. Different contexts, cultures, and points in the pandemic curve may have contributed to differing patterns of psychological outcomes amongst nurses in various Asia-Pacific countries. It is important that all health care workers practice self-care and render peer support to bolster psychological resilience for effective coping. In addition, this study also demonstrated the potential use of decision tree–based machine learning models and SHAP value plots in identifying contributing factors of sophisticated problems in the health care industry.

BACKGROUND As the COVID-19 pandemic evolves, challenges in frontline work continue to impose a significant psychological impact on nurses. However, there is a lack of data on how nurses fared compared to other health care workers in the Asia-Pacific region. OBJECTIVE This study aims to investigate (1) the psychological outcome characteristics of nurses in different Asia-Pacific countries and (2) psychological differences between nurses, doctors, and nonmedical health care workers. METHODS Exploratory data analysis and visualization were conducted on the data collected through surveys. A machine learning modeling approach was adopted to further discern the key psychological characteristics differentiating nurses from other health care workers. Decision tree–based machine learning models (Light Gradient Boosting Machine, GradientBoost, and RandomForest) were built to predict whether a set of psychological distress characteristics (ie, depression, anxiety, stress, intrusion, avoidance, and hyperarousal) belong to a nurse. Shapley Additive Explanation (SHAP) values were extracted to identify the prominent characteristics of each of these models. The common prominent characteristic among these models is akin to the most distinctive psychological characteristic that differentiates nurses from other health care workers. RESULTS Nurses had relatively higher percentages of having normal or unchanged psychological distress symptoms relative to other health care workers (n=233-260 [86.0%-95.9%] vs n=187-199 [74.8%-91.7%]). Among those without psychological symptoms, nurses constituted a higher proportion than doctors and nonmedical health care workers (n=194 [40.2%], n=142 [29.5%], and n=146 [30.3%], respectively). Nurses in Vietnam showed the highest level of depression, stress, intrusion, avoidance, and hyperarousal symptoms compared to those in Singapore, Malaysia, and Indonesia. Nurses in Singapore had the highest level of anxiety. In addition, nurses had the lowest level of stress, which is the most distinctive psychological outcome characteristic derived from machine learning models, compared to other health care workers. Data for India were excluded from the analysis due to the differing psychological response pattern observed in nurses in India. A large number of female nurses emigrating from South India could not have psychologically coped well without the support from family members while living alone in other states. CONCLUSIONS Nurses were least psychologically affected compared to doctors and other health care workers. Different contexts, cultures, and points in the pandemic curve may have contributed to differing patterns of psychological outcomes amongst nurses in various Asia-Pacific countries. It is important that all health care workers practice self-care and render peer support to bolster psychological resilience for effective coping. In addition, this study also demonstrated the potential use of decision tree–based machine learning models and SHAP value plots in identifying contributing factors of sophisticated problems in the health care industry.

Rationale: Because of its rapidly-growing and aging populations, the global burden of stroke will be felt most strongly in Asia. Data from Asia are scarce. Even rarer are data from multiethnic populations. Aim: The Singapore Stroke Registry is a countrywide registry of risk factors, stroke subtypes, management and outcome of incident and recurrent stroke in multiethnic Singapore. Methods and design: Using a cold-pursuit design, potential subjects are identified from medical claims, hospital discharge summaries and death certificates. Standardized methods are used for case ascertainment and data recording by trained staff into standardized case report forms. All ages and both genders are included. Study outcome: Stroke incidence, subtypes and mortality will be calculated. Trend data will be obtained. Inter-ethnic differences will be explored. Discussion: The Registry has many features of an ‘ideal’ incidence study. Previously unavailable countrywide data on an Asian population will be acquired. The unique data on inter-ethnic differences will help fill knowledge gaps in stroke epidemiology.