Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

Is the proportional recovery rule applicable to the lower limb in ischemic stroke patients?

This dataset consists of Fugl-Meyer Assessment scores for the upper and lower extremity measured within 72 hours and at 6 months post stroke

Development of a SARS-CoV-2 Total Antibody Assay and the Dynamics of Antibody Response over Time in Hospitalized and Nonhospitalized Patients with COVID-19

Severe acute respiratory syndrome coronavirus (SARS-CoV)-2 infections often cause only mild disease that may evoke relatively low Ab titers compared with patients admitted to hospitals. Generally, total Ab bridging assays combine good sensitivity with high specificity. Therefore, we developed sensitive total Ab bridging assays for detection of SARS-CoV-2 Abs to the receptor-binding domain (RBD) and nucleocapsid protein in addition to conventional isotype-specific assays. Ab kinetics was assessed in PCR-confirmed, hospitalized coronavirus disease 2019 (COVID-19) patients (n = 41) and three populations of patients with COVID-19 symptoms not requiring hospital admission: PCR-confirmed convalescent plasmapheresis donors (n = 182), PCR-confirmed hospital care workers (n = 47), and a group of longitudinally sampled symptomatic individuals highly suspect of COVID-19 (n = 14). In nonhospitalized patients, the Ab response to RBD is weaker but follows similar kinetics, as has been observed in hospitalized patients. Across populations, the RBD bridging assay identified most patients correctly as seropositive. In 11/14 of the COVID-19-suspect cases, seroconversion in the RBD bridging assay could be demonstrated before day 12; nucleocapsid protein Abs emerged less consistently. Furthermore, we demonstrated the feasibility of finger-prick sampling for Ab detection against SARS-CoV-2 using these assays. In conclusion, the developed bridging assays reliably detect SARS-CoV-2 Abs in hospitalized and nonhospitalized patients and are therefore well suited to conduct seroprevalence studies

Supplementary Material for: The Impact of Depressive Disorder Symptoms and Subtypes on 6-Year Incidence of Somatic Diseases

Medicin

Development of a SARS-CoV-2 Total Antibody Assay and the Dynamics of Antibody Response over Time in Hospitalized and Nonhospitalized Patients with COVID-19

Severe acute respiratory syndrome coronavirus (SARS-CoV)-2 infections often cause only mild disease that may evoke relatively low Ab titers compared with patients admitted to hospitals. Generally, total Ab bridging assays combine good sensitivity with high specificity. Therefore, we developed sensitive total Ab bridging assays for detection of SARS-CoV-2 Abs to the receptor-binding domain (RBD) and nucleocapsid protein in addition to conventional isotype-specific assays. Ab kinetics was assessed in PCR-confirmed, hospitalized coronavirus disease 2019 (COVID-19) patients (n = 41) and three populations of patients with COVID-19 symptoms not requiring hospital admission: PCR-confirmed convalescent plasmapheresis donors (n = 182), PCR-confirmed hospital care workers (n = 47), and a group of longitudinally sampled symptomatic individuals highly suspect of COVID-19 (n = 14). In nonhospitalized patients, the Ab response to RBD is weaker but follows similar kinetics, as has been observed in hospitalized patients. Across populations, the RBD bridging assay identified most patients correctly as seropositive. In 11/14 of the COVID-19-suspect cases, seroconversion in the RBD bridging assay could be demonstrated before day 12; nucleocapsid protein Abs emerged less consistently. Furthermore, we demonstrated the feasibility of finger-prick sampling for Ab detection against SARS-CoV-2 using these assays. In conclusion, the developed bridging assays reliably detect SARS-CoV-2 Abs in hospitalized and nonhospitalized patients and are therefore well suited to conduct seroprevalence studies

Using the biopsychosocial model for identifying subgroups of detained juveniles at different risk of re-offending in practice: a latent class regression analysis approach

Abstract Background Juvenile delinquents constitute a heterogeneous group, which complicates decision-making based on risk assessment. Various psychosocial factors have been used to define clinically relevant subgroups of juvenile offenders, while neurobiological variables have not yet been integrated in this context. Moreover, translation of neurobiological group differences to individual risk assessment has proven difficult. We aimed to identify clinically relevant subgroups associated with differential youth offending outcomes, based on psychosocial and neurobiological characteristics, and to test whether the resulting model can be used for risk assessment of individual cases. Methods A group of 223 detained juveniles from juvenile justice institutions was studied. Latent class regression analysis was used to detect subgroups associated with differential offending outcome (recidivism at 12 month follow-up). As a proof of principle, it was tested in a separate group of 76 participants whether individual cases could be assigned to the identified subgroups, using a prototype ‘tool’ for calculating class membership. Results Three subgroups were identified: a ‘high risk—externalizing’ subgroup, a ‘medium risk—adverse environment’ subgroup, and a ‘low risk—psychopathic traits’ subgroup. Within these subgroups, both autonomic nervous system and neuroendocrinological measures added differentially to the prediction of subtypes of reoffending (no, non-violent, violent). The ‘tool’ for calculating class membership correctly assigned 92.1% of participants to a class and reoffending risk. Conclusions The LCRA approach appears to be a useful approach to integrate neurobiological and psychosocial risk factors to identify subgroups with different re-offending risk within juvenile justice institutions. This approach may be useful in the development of a biopsychosocial assessment tool and may eventually help clinicians to assign individuals to those subgroups and subsequently tailor intervention based on their re-offending risk

Using the biopsychosocial model for identifying subgroups of detained juveniles at different risk of re-offending in practice: a latent class regression analysis approach

Abstract Background Juvenile delinquents constitute a heterogeneous group, which complicates decision-making based on risk assessment. Various psychosocial factors have been used to define clinically relevant subgroups of juvenile offenders, while neurobiological variables have not yet been integrated in this context. Moreover, translation of neurobiological group differences to individual risk assessment has proven difficult. We aimed to identify clinically relevant subgroups associated with differential youth offending outcomes, based on psychosocial and neurobiological characteristics, and to test whether the resulting model can be used for risk assessment of individual cases. Methods A group of 223 detained juveniles from juvenile justice institutions was studied. Latent class regression analysis was used to detect subgroups associated with differential offending outcome (recidivism at 12 month follow-up). As a proof of principle, it was tested in a separate group of 76 participants whether individual cases could be assigned to the identified subgroups, using a prototype ‘tool’ for calculating class membership. Results Three subgroups were identified: a ‘high risk—externalizing’ subgroup, a ‘medium risk—adverse environment’ subgroup, and a ‘low risk—psychopathic traits’ subgroup. Within these subgroups, both autonomic nervous system and neuroendocrinological measures added differentially to the prediction of subtypes of reoffending (no, non-violent, violent). The ‘tool’ for calculating class membership correctly assigned 92.1% of participants to a class and reoffending risk. Conclusions The LCRA approach appears to be a useful approach to integrate neurobiological and psychosocial risk factors to identify subgroups with different re-offending risk within juvenile justice institutions. This approach may be useful in the development of a biopsychosocial assessment tool and may eventually help clinicians to assign individuals to those subgroups and subsequently tailor intervention based on their re-offending risk

The frail older person does not exist: development of frailty profiles with latent class analysis

Abstract Background A fundamental issue in elderly care is targeting those older people at risk and in need of care interventions. Frailty is widely used to capture variations in health risks but there is no general consensus on the conceptualization of frailty. Indeed, there is considerable heterogeneity in the group of older people characterized as frail. This research identifies frailty profiles based on the physical, psychological, social and cognitive domains of functioning and the severity of the problems within these domains. Methods This research was a secondary data-analysis of older persons derived from The Older Person and Informal Caregiver Minimum Dataset. Selected respondents were 60 years and older (n =â 43,704; 59.6% female). The following variables were included: self-reported health, cognitive functioning, social functioning, mental health, morbidity status, and functional limitations. Using latent class analysis, the population was divided in subpopulations that were subsequently discussed in a focus group with older people for further validation. Results We distinguished six frailty profiles: relatively healthy; mild physically frail; psychologically frail; severe physically frail; medically frail and multi-frail. The relatively healthy had limited problems across all domains. In three profiles older people mostly had singular problems in either the physical or psychological domain and the severity of the problems differed. Two remaining profiles were multidimensional with a combination of problems that extended to the social and cognitive domains. Conclusions Our research provides an empirical base for meaningful frailty profiles. The profiles showed specific patterns underlying the problems in different domains of functioning. The heterogeneous population of frail older people has differing needs and faces different health issues that should be considered to tailor care interventions. Evaluation research of these interventions should acknowledge the heterogeneity of frailty by profiling