A novel approach to accurately measuring the burden of hospitalisations for cardiovascular disease in people with diabetes: A pilot study.

AIM: To determine the reliability of hospital discharge codes for heart failure (HF), acute myocardial infarction (AMI) and stroke compared with adjudicated diagnosis, and to pilot a scalable approach to adjudicate records on a population-based sample. METHODS: A population-based sample of 685 people with diabetes admitted (1274 admissions) to one of three Australian hospitals during 2018-2020 were randomly selected for this study. All medical records were reviewed and adjudicated. RESULTS: Cardiovascular diseases were the most common primary reason for hospitalisation in people with diabetes, accounting for ~17% (215/1274) of all hospitalisations, with HF as the leading cause. ICD-10 codes substantially underestimated HF prevalence and had the lowest agreement with the adjudicated diagnosis of HF (Kappa = 0.81), compared with AMI and stroke (Kappa ≥ 0.91). While ICD-10 codes provided suboptimal sensitivity (72%) for HF, the performance was better for AMI (sensitivity 84%; specificity 100%) and stroke (sensitivity 85%; specificity 100%). A novel approach to screen possible HF cases only required adjudicating 8% (105/1274) of records, correctly identified 78/81 of HF admissions and yielded 96% sensitivity and 98% specificity. CONCLUSIONS: While ICD-10 codes appear reliable for AMI or stroke, a more complex diagnosis such as HF benefits from a two-stage process to screen for suspected HF cases that need adjudicating. The next step is to validate this novel approach on large multi-centre studies in diabetes.

Variables impacting prolonged post-anesthesia care unit length of stay in gynecologic cancer patients in the era of same day minimally invasive hysterectomy.

OBJECTIVES: Minimally invasive surgery for treatment of gynecologic malignancies is associated with decreased pain, fewer complications, earlier return to activity, lower cost, and shorter hospital stays. Patients are often discharged the day of surgery, but occasionally stay overnight due to prolonged post-anesthesia care unit (PACU) stays. The objective of this study was to identify risk factors for prolonged PACU length of stay (LOS). METHODS: This is a single institution retrospective review of patients who underwent minimally invasive hysterectomy for gynecologic cancer from 2019 to 2022 and had a hospital stay <24-h. The primary outcome was PACU LOS. Demographics, pre-operative diagnoses, and surgical characteristics were recorded. After Box-Cox transformation, linear regression was used to determine significant predictors of PACU LOS. RESULTS: For the 661 patients identified, median PACU LOS was 5.04 h (range 2.16-23.76 h). On univariate analysis, longer PACU LOS was associated with increased age (ρ = 0.106, p = 0.006), non-partnered status [mean difference (MD) = 0.019, p = 0.099], increased alcohol use (MD = 0.018, p = 0.102), increased Charlson Comorbidity Index (CCI) score (ρ = 0.065, p = 0.097), and ASA class ≥3 (MD = 0.033, p = 0.002). Using multivariate linear regression, increased age (R2 = 0.0011, p = 0.043), non-partnered status (R2 = 0.0389, p < 0.001), and ASA class ≥3 (R2 = 0.0250, p = 0.023) were associated with increased PACU LOS. CONCLUSIONS: Identifying patients at risk for prolonged PACU LOS, including patients who are older, non-partnered, and have an ASA class ≥3, may allow for interventions to improve patient experience, better utilize hospital resources, decrease PACU overcrowding, and limit postoperative admissions and complications. The relationship between non-partnered status and PACU LOS is the most novel relationship identified in this study.

A comparison of cohorts of children with cerebral palsy from a population register and hospital admission data: A data linkage study.

BACKGROUND: Administrative health data, such as hospital admission data, are often used in research to identify children/young people with cerebral palsy (CP). OBJECTIVES: To compare sociodemographic, clinical details and mortality of children/young people identified as having CP in either a CP population registry or hospital admission data. METHODS: We identified two cohorts of children/young people (birth years 2001-2010, age at study end or death 2 months to 19 years 6 months) with a diagnosis of CP from either (i) the New South Wales (NSW)/Australian Capital Territory (ACT) CP Register or (ii) NSW hospital admission data (2001-2020). Using record linkage, these data sources were linked to each other and NSW Death, Perinatal, and Disability datasets. We determined the sensitivity and positive predictive value (PPV) of CP diagnosis in hospital admission data compared with the NSW/ACT CP Register (gold standard). We then compared the sociodemographic and clinical characteristics and mortality of the two cohorts available through record linkage using standardised mean difference (SMD). RESULTS: There were 1598 children/young people with CP in the NSW/ACT CP Register and 732-2439 children/young people with CP in hospital admission data, depending on the case definition used. The sensitivity of hospital admission data for diagnosis of CP ranged from 0.40-0.74 and PPV 0.47-0.73. Compared with children/young people with CP identified in the NSW/ACT CP Register, a greater proportion of those identified in hospital admission data (one or more admissions with G80 case definition) were older, lived in major cities, had comorbidities including epilepsy, gastrostomy use, intellectual disability and autism, and died during the study period (SMD > 0.1). CONCLUSIONS: Sociodemographic and clinical characteristics differ between cohorts of children/young people with CP identified using a CP register or hospital admission data. Those identified in hospital admission data have higher rates of comorbidities and death, suggesting some may have progressive conditions and not CP. These differences should be considered when planning and interpreting research using various data sources.

Single center assessment of the role of Oakland score among patients admitted for acute lower gastrointestinal bleeding.

BACKGROUND/OBJECTIVES: The Oakland score was developed to predict safe discharge in patients who present to the emergency department with lower gastrointestinal bleeding (LGIB). In this study, we retrospectively evaluated if this score can be implemented to assess safe discharge (score ≤ 10) at WellStar Atlanta Medical Center (WAMC). METHODS: A retrospective cohort study of 108 patients admitted at WAMC from January 1, 2020 to December 30, 2021 was performed. Patients with LGIB based on the ICD-10 codes were included. Oakland score was calculated using 7 variables (age, sex, previous LGIB, digital rectal exam, pulse, systolic blood pressure (SBP) and hemoglobin (Hgb)) for all patients at admission and discharge from the hospital. The total score ranges from 0 to 35 and a score of ≤ 10 is a cut-off that has been shown to predict safe discharge. Hgb and SBP are the main contributors to the score, where lower values correspond to a higher Oakland score. Descriptive and multivariate analysis was performed using SPSS 23 software. RESULTS: A total of 108 patients met the inclusion criteria, 53 (49.1%) were female with racial distribution was as follows: 89 (82.4%) African Americans, 17 (15.7%) Caucasian, and 2 (1.9%) others. Colonoscopy was performed in 69.4% patients; and 61.1% patients required blood transfusion during hospitalization. Mean SBP records at admission and discharge were 129.0 (95% CI, 124.0-134.1) and 130.7 (95% CI,125.7-135.8), respectively. The majority (59.2%) of patients had baseline anemia and the mean Hgb values were 11.0 (95% CI, 10.5-11.5) g/dL at baseline prior to hospitalization, 8.8 (95% CI, 8.2-9.5) g/dL on arrival and 9.4 (95% CI, 9.0-9.7) g/dL at discharge from hospital. On admission, 100/108 (92.6%) of patients had an Oakland score of > 10 of which almost all patients (104/108 (96.2%)) continued to have persistent elevation of Oakland Score greater than 10 at discharge. Even though, the mean Oakland score improved from 21.7 (95% CI, 20.4-23.1) of the day of arrival to 20.3 (95% CI, 19.4-21.2) at discharge, only 4/108 (3.7%) of patients had an Oakland score of ≤ 10 at discharge. Despite this, only 9/108 (8.33%) required readmission for LGIB during a 1-year follow-up. We found that history of admission for previous LGIB was associated with readmission with adjusted odds ratio 4.42 (95% CI, 1.010-19.348, p = 0.048). CONCLUSIONS: In this study, nearly all patients who had Oakland score of > 10 at admission continued to have a score above 10 at discharge. If the Oakland Score was used as the sole criteria for discharge most patients would not have met discharge criteria. Interestingly, most of these patients did not require readmission despite an elevated Oakland score at time of discharge, indicating the Oakland score did not really predict safe discharge. A potential confounder was the Oakland score did not consider baseline anemia during calculation. A prospective study to evaluate a modified Oakland score that considers baseline anemia could add value in this patient population.

Ambient PM2.5 Chemical Composition and Cardiovascular Disease Hospitalizations in China.

Little is known about the impacts of specific chemical components on cardiovascular hospitalizations. We examined the relationships of PM2.5 chemical composition and daily hospitalizations for cardiovascular disease in 184 Chinese cities. Acute PM2.5 chemical composition exposures were linked to higher cardiovascular disease hospitalizations on the same day and the percentage change of cardiovascular admission was the highest at 1.76% (95% CI, 1.36-2.16%) per interquartile range increase in BC, followed by 1.07% (0.72-1.43%) for SO42-, 1.04% (0.63-1.46%) for NH4+, 0.99% (0.55-1.43%) for NO3-, 0.83% (0.50-1.17%) for OM, and 0.80% (0.34%-1.26%) for Cl-. Similar findings were observed for all cause-specific major cardiovascular diseases, except for heart rhythm disturbances. Short-term exposures to PM2.5 chemical composition were related to higher admissions and showed diverse impacts on major cardiovascular diseases.

The incidence and prevalence of proton pump inhibitor usage among internal medicine patients after hospital admission: A retrospective cohort study.

BACKGROUND: The use of proton pump inhibitors (PPIs) has increased over the past decades. One potential gateway into new PPI use is following a hospital admission. The study aimed to examine the incidence of new PPI usage following admission to internal medicine services and the ratio of new persistent users. METHODS: A retrospective descriptive study was conducted among all adults who had been admitted to internal medicine wards at the National University Hospital of Iceland from 2010-2020. Data was obtained from the Icelandic Internal Medicine Database. The proportion of patients who started treatment with PPI within 3 months of discharge (new users) and the proportion of patients who continued to use it after 3 months (persistent users) were examined. RESULTS: Among 85.942 admissions during the study period, 7238 (15.6%) became new users, and of those 4942 (68%) were new persistent users. The incidence of new PPI use was highest for patients discharged from gastroenterology (32.2%), hematology (31.8%), and oncology (29.2%). Patients with new PPI use more commonly had a history of malignancy (19.5%) and liver disease (22.7%) and more commonly were admitted to the ICU during their hospitalization. The highest ratio of persistent usage was among patients discharged from geriatric medicine (84%). CONCLUSION: One in every six patients admitted to internal medicine wards filled out a prescription for PPI within 3 months from discharge, and a large proportion of them became persistent users. The high rate of new PPI users from oncology and hematology is noteworthy and requires further research.

The impact of heat waves and cold spells on pneumonia risk: A nationwide study.

Climate change affects human health and has been linked to several infectious diseases in recent year. However, there is limited assessment on the impact of heat waves and cold spells on pneumonia risk. This study aims to examine the association of heat waves and cold spells with daily pneumonia hospitalizations in 168 cities in China. Data on pneumonia hospitalizations between 2014 and 2017 were extracted from a national claim database of 280 million beneficiaries. We consider combining temperature intensity and duration to define heat waves and cold spells.This association was quantified using a quasi-Poisson generalized linear model combined with a distributed lag nonlinear model. Exposure-response curves and potential effect modifiers were also estimated. We found that the peak relative risk (RR) of cold spells on daily hospitalizations for pneumonia was observed in relatively mild cold spells with a threshold below the 3 days at the 2nd percentile (RR = 1.69, 95% CI: 1.46-1.92). The risk of heat waves increased with the thresholds, and the greatest risk was found for extremely heatwave period of 4 days at the 98th percentile (RR = 1.69, 95% CI: 1.46-1.92). Heat waves and cold spells are more likely to adversely affect women. In conclusion, our study provided novel and strong evidence that exposure to heat waves and cold spells was associate with increased hospital visits for pneumonia, especially in females. This is the first national study in China to comprehensively evaluate the influence of heat waves and cold spells on pneumonia risk, and the findings may offer valuable insights into the impact of climate change on public health.

Impact of delirium on acute stroke outcomes: A systematic review and meta-analysis.

Delirium is a common complication in acute stroke patients. A 2011 meta-analysis showed an increased risk of in-hospital mortality and mortality within 12 months post-stroke, longer hospitalization durations, and increased likelihood of being discharged to a nursing home for patients experiencing post-stroke delirium. There is a need for an updated meta-analysis with several new studies having been since published. The PubMed and Scopus databases were screened for relevant studies. Inclusion criteria were as follows: retrospective or prospective studies reporting on the effects of delirium accompanying acute stroke on mortality, functional outcomes, length of hospital stay and need for re-admission. Strength of association was presented as pooled adjusted relative risk (RR) for categorical outcomes and weighted mean difference (WMD) for continuous outcomes. Statistical analysis was done using STATA version 16.0. The meta-analysis included 22 eligible articles. Eighteen of the 22 studies were prospective follow ups. Included studies were of good quality. Post-stroke delirium was associated with increased risk of in-hospital mortality, as well as mortality within 12 months post-stroke. Patients with delirium experienced increased hospital stay durations, were at greater risk for hospital readmission, and showed elevated risk for poor functional outcome. Compared to those who did not have delirium, stroke patients with delirium were 42% less likely to be discharged to home. Acute stroke patients with delirium are at an increased risk for poor short- and long-term outcomes. More research is needed to identify the best set of interventions to manage such patients and improve outcomes.

Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data.

Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be easily utilized by emergency department physicians. We presented machine learning models that predicted the likelihood of hospitalizations within 24 hours and estimated waiting times. Moreover, we revealed the enhanced performance of these machine learning models compared to existing models by incorporating unstructured text data. Among several evaluated models, the extreme gradient boosting model that incorporated text data yielded the best performance. This model achieved an area under the receiver operating characteristic curve score of 0.922 and an area under the precision-recall curve score of 0.687. The mean absolute error revealed a difference of approximately 3 hours. Using this model, we classified the probability of patients not being admitted within 24 hours as Low, Medium, or High and identified important variables influencing this classification through explainable artificial intelligence. The model results are readily displayed on an electronic dashboard to support the decision-making of emergency department physicians and alleviate overcrowding, thereby resulting in socioeconomic benefits for medical facilities.

Age- and sex-specific associations of frailty with mortality and healthcare utilization in community-dwelling adults from ontario, Canada.

BACKGROUND: Understanding how health trajectories are related to the likelihood of adverse outcomes and healthcare utilization is key to planning effective strategies for improving health span and the delivery of care to older adults. Frailty measures are useful tools for risk stratification in community-based and primary care settings, although their effectiveness in adults younger than 60 is not well described. METHODS: We performed a 10-year retrospective analysis of secondary data from the Ontario Health Study, which included 161,149 adults aged ≥ 18. Outcomes including all-cause mortality and hospital admissions were obtained through linkage to ICES administrative databases with a median follow-up of 7.1-years. Frailty was characterized using a 30-item frailty index. RESULTS: Frailty increased linearly with age and was higher for women at all ages. A 0.1-increase in frailty was significantly associated with mortality (HR = 1.47), the total number of outpatient (IRR = 1.35) and inpatient (IRR = 1.60) admissions over time, and length of stay (IRR = 1.12). However, with exception to length of stay, these estimates differed depending on age and sex. The hazard of death associated with frailty was greater at younger ages, particularly in women. Associations with admissions also decreased with age, similarly between sexes for outpatient visits and more so in men for inpatient. CONCLUSIONS: These findings suggest that frailty is an important health construct for both younger and older adults. Hence targeted interventions to reduce the impact of frailty before the age of 60 would likely have important economic and social implications in both the short- and long-term.

Patient, caregiver and professional views on preventable emergency admissions of older patients, a multi-method study in three Dutch hospitals.

BACKGROUND: Older adults are too often hospitalized from the emergency department (ED) without needing hospital care. Knowledge about rates and causes of these preventable emergency admissions (PEAs) is limited. This study aimed to assess the proportion of PEAs, the level of agreement on perceived preventability between physicians and patients, and to explore their underlying causes as perceived by patients, their relatives, and the admitting physician. METHODS: A multi-center multi-method study at the ED of one academic and two regional hospitals in the Netherlands was performed. All patients aged > 70 years and hospitalized from the ED were consecutively sampled during a six-week period. Quantitative data regarding patient and clinical characteristics and perceived preventability of the admission were prospectively collected from the electronical medical record and analyzed using descriptive statistics. Agreement on preventability between patient, caregivers and physicians was assessed by using the Cohen's kappa. Underlying causes of a PEA were subsequently collected by semi-structured interviews with patients and caregivers. Physician's perceived causes of a PEA were collected by telephone interviews and by open-ended questions sent by email. Thematic content analysis was used to analyze the interview transcripts and email narratives. RESULTS: Out of 773 admissions, 56 (7.2%) were deemed preventable by patients or their caregivers. Admitting physicians regarded 75 (9.7%) admissions as preventable. The level of agreement between these two groups was low with a Cohen's kappa score of 0.10 (p = 0.003). Perceived causes for PEAs related to six themes: (1) insufficient support at home, (2) suboptimal care in the community setting, (3) errors in hospital care, (4) time of presentation to ED and availability of resources, (5) delayed help seeking behavior, and (6) errors made by patients. CONCLUSIONS: Our findings contribute to the existing evidence that a substantial part (almost one out of ten) of the older adults visiting the ED is perceived as unnecessary hospital care by patients, caregivers and health care providers. Findings also provide valuable insight into the causes for PEAs from a patient perspective. Further research is needed to understand why the perspectives of those responsible for hospital admission and those being admitted vary considerably.

Recognition of cognitive dysfunction in hospitalised older patients: a flash mob study.

BACKGROUND: It is important that healthcare professionals recognise cognitive dysfunction in hospitalised older patients in order to address associated care needs, such as enhanced involvement of relatives and extra cognitive and functional support. However, studies analysing medical records suggest that healthcare professionals have low awareness of cognitive dysfunction in hospitalised older patients. In this study, we investigated the prevalence of cognitive dysfunction in hospitalised older patients, the percentage of patients in which cognitive dysfunction was recognised by healthcare professionals, and which variables were associated with recognition. METHODS: A multicentre, nationwide, cross-sectional observational study was conducted on a single day using a flash mob study design in thirteen university and general hospitals in the Netherlands. Cognitive function was assessed in hospitalised patients aged ≥ 65 years old, who were admitted to medical and surgical wards. A Mini-Cog score of < 3 out of 5 indicated cognitive dysfunction. The attending nurses and physicians were asked whether they suspected cognitive dysfunction in their patient. Variables associated with recognition of cognitive dysfunction were assessed using multilevel and multivariable logistic regression analyses. RESULTS: 347 of 757 enrolled patients (46%) showed cognitive dysfunction. Cognitive dysfunction was recognised by attending nurses in 137 of 323 patients (42%) and by physicians in 156 patients (48%). In 135 patients (42%), cognitive dysfunction was not recognised by either the attending nurse or physician. Recognition of cognitive dysfunction was better at a lower Mini-Cog score, with the best recognition in patients with the lowest scores. Patients with a Mini-Cog score < 3 were best recognised in the geriatric department (69% by nurses and 72% by physicians). CONCLUSION: Cognitive dysfunction is common in hospitalised older patients and is poorly recognised by healthcare professionals. This study highlights the need to improve recognition of cognitive dysfunction in hospitalised older patients, particularly in individuals with less apparent cognitive dysfunction. The high proportion of older patients with cognitive dysfunction suggests that it may be beneficial to provide care tailored to cognitive dysfunction for all hospitalised older patients.

Reasons for hospitalisation and cumulative mortality in people, 75 years or older, at high risk of hospital admission: a prospective study.

BACKGROUND: A small proportion of the older population accounts for a high proportion of healthcare use. For effective use of limited healthcare resources, it is important to identify the group with greatest needs. The aim of this study was to explore frequency and reason for hospitalisation and cumulative mortality, in an older population at predicted high risk of hospital admission, and to assess if a prediction model can be used to identify individuals with the greatest healthcare needs. Furthermore, discharge diagnoses were explored to investigate if they can be used as basis for specific interventions in the high-risk group. METHODS: All residents, 75 years or older, living in Östergötland, Sweden, on January 1st, 2017, were included. Healthcare data from 2016 was gathered and used by a validated prediction model to create risk scores for hospital admission. The population was then divided into groups by percentiles of risk. Using healthcare data from 2017-2018, two-year cumulative incidence of hospitalisation was analysed using Gray´s test. Cumulative mortality was analysed with the Kaplan-Meier method and primary discharge diagnoses were analysed with standardised residuals. RESULTS: Forty thousand six hundred eighteen individuals were identified (mean age 82 years, 57.8% women). The cumulative incidence of hospitalisation increased with increasing risk of hospital admission (24% for percentiles < 60 to 66% for percentiles 95-100). The cumulative mortality also increased with increasing risk (7% for percentiles < 60 to 43% for percentiles 95-100). The most frequent primary discharge diagnoses for the population were heart diseases, respiratory infections, and hip injuries. The incidence was significantly higher for heart diseases and respiratory infections and significantly lower for hip injuries, for the population with the highest risk of hospital admission (percentiles 85-100). CONCLUSIONS: Individuals 75 years or older, with high risk of hospital admission, were demonstrated to have considerable higher cumulative mortality as well as incidence of hospitalisation. The results support the use of the prediction model to direct resources towards individuals with highest risk scores, and thus, likely the greatest care needs. There were only small differences in discharge diagnoses between the risk groups, indicating that interventions to reduce hospitalisations should be personalised. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT03180606, first posted 08/06/2017.

Clinical characteristics and prognostic marker for hospitalization in children with influenza infection in an emergency setting.

BACKGROUND: Influenza is a main cause of illnesses during seasonal outbreaks. Identifying children with influenza who may need hospitalization may lead to better influenza outcomes. OBJECTIVE: To identify factors associated with the severity of influenza infection, specifically among children who were admitted to the hospital after being diagnosed with influenza at the emergency department. METHODS: A retrospective cohort study was conducted among pediatric patients (age < 18 years) with a positive influenza rapid test who visited the emergency department at Srinagarind hospital between January2015-December2019. The dependent variable was hospital admission, while the independent variables included clinical parameters, laboratory results, and emergency severity index(ESI). The association between these variables and hospital admission was analyzed. RESULTS: There were 542 cases of influenza included in the study. The mean age was 7.50 ± 4.52 years. Males accounted for 52.4% of the cases. A total of 190(35.05%) patients, needed hospitalization. Patients with pneumonia, those who required hospitalization or were admitted to the critical care unit, consistently exhibited an elevated absolute monocyte count and a reduced lymphocyte-to-monocyte ratio (LMR). Various factors contribute to an increased risk for hospitalization, including ESI level 1-2, co-morbidity in patients, age < 1 year old, and an LMR below 2. CONCLUSIONS: ESI level 1-2 and co-morbidity in patients represent significant risk factors that contribute to higher hospitalization admissions. A LMR below 2 can be used as a prognostic marker for hospitalization in children with influenza infection.

Factors predictive of hospital admission for children via emergency departments in Australia and Sweden: an observational cross-sectional study.

BACKGROUND: Identifying factors predictive of hospital admission can be useful to prospectively inform bed management and patient flow strategies and decrease emergency department (ED) crowding. It is largely unknown if admission rate or factors predictive of admission vary based on the population to which the ED served (i.e., children only, or both adults and children). This study aimed to describe the profile and identify factors predictive of hospital admission for children who presented to four EDs in Australia and one ED in Sweden. METHODS: A multi-site observational cross-sectional study using routinely collected data pertaining to ED presentations made by children < 18 years of age between July 1, 2011 and October 31, 2012. Univariate and multivariate analysis were undertaken to determine factors predictive of hospital admission. RESULTS: Of the 151,647 ED presentations made during the study period, 22% resulted in hospital admission. Admission rate varied by site; the children's EDs in Australia had higher admission rates (South Australia: 26%, Queensland: 23%) than the mixed (adult and children's) EDs (South Australia: 13%, Queensland: 17%, Sweden: 18%). Factors most predictive of hospital admission for children, after controlling for triage category, included hospital type (children's only) adjusted odds ratio (aOR):2.3 (95%CI: 2.2-2.4), arrival by ambulance aOR:2.8 (95%CI: 2.7-2.9), referral from primary health aOR:1.5 (95%CI: 1.4-1.6) and presentation with a respiratory or gastrointestinal condition (aOR:2.6, 95%CI: 2.5-2.8 and aOR:1.5, 95%CI: 1.4-1.6, respectively). Predictors were similar when each site was considered separately. CONCLUSIONS: Although the characteristics of children varied by site, factors predictive of hospital admission were mostly similar. The awareness of these factors predicting the need for hospital admission can support the development of clinical pathways.

Effect of a Structured Multilevel Telehealth Service on Hospital Admissions and Mortality During COVID-19 in a Resource-Limited Region in Brazil: Retrospective Cohort Study.

BACKGROUND: The COVID-19 pandemic represented a great stimulus for the adoption of telehealth and many initiatives in this field have emerged worldwide. However, despite this massive growth, data addressing the effectiveness of telehealth with respect to clinical outcomes remain scarce. OBJECTIVE: The aim of this study was to evaluate the impact of the adoption of a structured multilevel telehealth service on hospital admissions during the acute illness course and the mortality of adult patients with flu syndrome in the context of the COVID-19 pandemic. METHODS: A retrospective cohort study was performed in two Brazilian cities where a public COVID-19 telehealth service (TeleCOVID-MG) was deployed. TeleCOVID-MG was a structured multilevel telehealth service, including (1) first response and risk stratification through a chatbot software or phone call center, (2) teleconsultations with nurses and medical doctors, and (3) a telemonitoring system. For this analysis, we included data of adult patients registered in the Flu Syndrome notification databases who were diagnosed with flu syndrome between June 1, 2020, and May 31, 2021. The exposed group comprised patients with flu syndrome who used TeleCOVID-MG at least once during the illness course and the control group comprised patients who did not use this telehealth service during the respiratory illness course. Sociodemographic characteristics, comorbidities, and clinical outcomes data were extracted from the Brazilian official databases for flu syndrome, Severe Acute Respiratory Syndrome (due to any respiratory virus), and mortality. Models for the clinical outcomes were estimated by logistic regression. RESULTS: The final study population comprised 82,182 adult patients with a valid registry in the Flu Syndrome notification system. When compared to patients who did not use the service (n=67,689, 82.4%), patients supported by TeleCOVID-MG (n=14,493, 17.6%) had a lower chance of hospitalization during the acute respiratory illness course, even after adjusting for sociodemographic characteristics and underlying medical conditions (odds ratio [OR] 0.82, 95% CI 0.71-0.94; P=.005). No difference in mortality was observed between groups (OR 0.99, 95% CI 0.86-1.12; P=.83). CONCLUSIONS: A telehealth service applied on a large scale in a limited-resource region to tackle COVID-19 was related to reduced hospitalizations without increasing the mortality rate. Quality health care using inexpensive and readily available telehealth and digital health tools may be delivered in areas with limited resources and should be considered as a potential and valuable health care strategy. The success of a telehealth initiative relies on a partnership between the involved stakeholders to define the roles and responsibilities; set an alignment between the different modalities and levels of health care; and address the usual drawbacks related to the implementation process, such as infrastructure and accessibility issues.

Early Detection of Pulmonary Embolism in a General Patient Population Immediately Upon Hospital Admission Using Machine Learning to Identify New, Unidentified Risk Factors: Model Development Study.

BACKGROUND: Under- or late identification of pulmonary embolism (PE)-a thrombosis of 1 or more pulmonary arteries that seriously threatens patients' lives-is a major challenge confronting modern medicine. OBJECTIVE: We aimed to establish accurate and informative machine learning (ML) models to identify patients at high risk for PE as they are admitted to the hospital, before their initial clinical checkup, by using only the information in their medical records. METHODS: We collected demographics, comorbidities, and medications data for 2568 patients with PE and 52,598 control patients. We focused on data available prior to emergency department admission, as these are the most universally accessible data. We trained an ML random forest algorithm to detect PE at the earliest possible time during a patient's hospitalization-at the time of his or her admission. We developed and applied 2 ML-based methods specifically to address the data imbalance between PE and non-PE patients, which causes misdiagnosis of PE. RESULTS: The resulting models predicted PE based on age, sex, BMI, past clinical PE events, chronic lung disease, past thrombotic events, and usage of anticoagulants, obtaining an 80% geometric mean value for the PE and non-PE classification accuracies. Although on hospital admission only 4% (1942/46,639) of the patients had a diagnosis of PE, we identified 2 clustering schemes comprising subgroups with more than 61% (705/1120 in clustering scheme 1; 427/701 and 340/549 in clustering scheme 2) positive patients for PE. One subgroup in the first clustering scheme included 36% (705/1942) of all patients with PE who were characterized by a definite past PE diagnosis, a 6-fold higher prevalence of deep vein thrombosis, and a 3-fold higher prevalence of pneumonia, compared with patients of the other subgroups in this scheme. In the second clustering scheme, 2 subgroups (1 of only men and 1 of only women) included patients who all had a past PE diagnosis and a relatively high prevalence of pneumonia, and a third subgroup included only those patients with a past diagnosis of pneumonia. CONCLUSIONS: This study established an ML tool for early diagnosis of PE almost immediately upon hospital admission. Despite the highly imbalanced scenario undermining accurate PE prediction and using information available only from the patient's medical history, our models were both accurate and informative, enabling the identification of patients already at high risk for PE upon hospital admission, even before the initial clinical checkup was performed. The fact that we did not restrict our patients to those at high risk for PE according to previously published scales (eg, Wells or revised Genova scores) enabled us to accurately assess the application of ML on raw medical data and identify new, previously unidentified risk factors for PE, such as previous pulmonary disease, in general populations.

Development and Validation of a Robust and Interpretable Early Triaging Support System for Patients Hospitalized With COVID-19: Predictive Algorithm Modeling and Interpretation Study.

BACKGROUND: Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability. OBJECTIVE: The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers. METHODS: We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods. RESULTS: Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910). CONCLUSIONS: RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice.

Impact of Comprehensive Primary Care in Patients With Complex Chronic Diseases: Nationwide Cohort Database Analysis in Korea.

BACKGROUND: More comprehensive healthcare services should be provided to patients with complex chronic diseases to better manage their complex care needs. This study examined the effectiveness of comprehensive primary care in patients with complex chronic diseases. METHODS: We obtained 2002-2019 data from the National Health Insurance Sample Cohort Database. Participants were individuals aged ≥ 30 years with at least two of the following diseases: hypertension, diabetes mellitus, and hyperlipidemia. Doctors' offices were classified into specialized, functional, and gray-zone based on patient composition and major diagnostic categories. The Cox proportional hazard model was used to examine the association between office type and hospital admission due to all-causes, severe cardiovascular or cerebrovascular diseases (CVDs), hypertension, diabetes mellitus, or hyperlipidemia. RESULTS: The mean patient age was 60.3 years; 55.8% were females. Among the 24,906 patients, 12.8%, 38.3%, and 49.0% visited specialized, functional, and gray-zone offices, respectively. Patients visiting functional offices had a lower risk of all-cause admission (hazard ratio [HR], 0.935; 95% confidence interval [CI], 0.895-0.976) and CVD-related admission (HR, 0.908; 95% CI, 0.844-0.977) than those visiting specialized offices. However, the admission risks for hypertension, diabetes mellitus, and hyperlipidemia were not significantly different among office types. CONCLUSION: This study provides evidence of the effectiveness of primary care in functional doctors' offices for patients with complex chronic diseases beyond a single chronic disease and suggests the need for policies to strengthen functional offices providing comprehensive care.

The lived experiences of patients with severe obesity during hospital admissions in Norway: A phenomenological hermeneutic study.

AIM: To explore the lived experiences of patients with severe obesity during hospital admissions. DESIGN: Qualitative study design. METHODS: Semi-structured individual interviews with 14 participants with severe obesity from Norway were conducted between May and October 2021. A qualitative phenomenological hermeneutical approach inspired by Paul Riceour was used to analyse the data. RESULTS: The following three themes were identified through the analysis of the lived experiences of patients with severe obesity during hospital admissions: blaming my weight, being prejudged and feeling different. The participants shared various emotional experiences of encounters with healthcare professionals at hospitals. They struggled to be recognized and welcomed like everyone else and found it difficult to be judged by someone who did not know them. The various experiences resulted in a vicious circle, ultimately leading to a fear of future hospitalization. CONCLUSION: Being a patient with obesity in a hospital setting can present various challenges, leading to feelings of shame and guilt. Experiences of stigma may not necessarily be related to the overall hospital context but rather to encounters with healthcare professionals who may be unfamiliar with the patient's history, which can lead to stigmatizing behaviours. IMPACT: Understanding how patients with severe obesity experience their hospital admissions and the importance of familiarizing themselves with the individual patients to avoid stigmatizing behaviours is important for healthcare professionals caring for obese patients. REPORTING METHOD: Consolidated Criteria for Reporting Qualitative Research. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. IMPACT STATEMENT: Patients with obesity often encounter stigmatization and negative attitudes from healthcare professionals, particularly in primary care settings. Patients with severe obesity experienced various challenging encounters with healthcare professionals during hospital admissions, resulting in a vicious circle, ultimately leading to a fear of future hospitalization. It is crucial for healthcare professionals involved in the care of patients with obesity to acquaint themselves with individual patients to prevent stigmatizing behaviours.