Post-operative critical care management of patients undergoing cytoreductive surgery and heated intraperitoneal chemotherapy (HIPEC)

<p>Abstract</p> <p>Background</p> <p>Cytoreductive surgery (CRS) and Heated Intraperitoneal Chemotherapy (HIPEC) results in a number of physiological changes with effects on the cardiovascular system, oxygen consumption and coagulation. The Critical Care interventions required by this cohort of patients have not yet been quantified.</p> <p>Methods</p> <p>This retrospective audit examines the experience of a Specialist Tertiary Centre in England over an 18 month period (January 2009-June 2010) during which 69 patients underwent CRS and HIPEC. All patients were extubated in the operating theatre and transferred to the Critical Care Unit (CCU) for initial post-operative management.</p> <p>Results</p> <p>Patients needed to remain on the CCU for 2.4 days (0.8-7.8). There were no 30 day mortalities. The majority of patients (70.1%) did not require post-operative organ support. 2 patients who developed pneumonia post-operatively required respiratory support. 18 (26.1%) patients required vasopressor support with norepinephrine with a mean duration of 13.94 hours (5-51 hours) and mean dose of 0.04 mcg/kg/min. Post-operative coagulopathy peaked at 24 hours. A significant drop in serum albumin was observed.</p> <p>Conclusion</p> <p>The degree of organ support required post-operatively is minimal. Early extubation is efficacious with the aid of epidural analgesia. Critical Care monitoring for 48 hours is desirable in view of the post-operative challenges.</p

Sleep disturbances in australian Vietnam veterans with and without posttraumatic stress disorder

Study Objectives: Posttraumatic stress disorder (PTSD) is a condition that may develop after a traumatic event, particularly combat-related trauma. Although sleep disturbance is a hallmark of PTSD, the prevalence of sleep disturbances in Australian veterans with PTSD remains uncertain. This study aimed to subjectively compare the prevalence of sleep disturbances in Australian Vietnam veterans with and without PTSD. Methods: A cross-sectional cohort study compared trauma-exposed Australian Vietnam veterans with and without PTSD. PTSD diagnosis was confrmed using the Clinician Administered PTSD Scale for DSM-5. Sleep information was evaluated using supervised structured questionnaires, including Epworth Sleepiness Scale (ESS) and Berlin and Mayo Questionnaires. Results: Two hundred fourteen male Vietnam veterans (108 with PTSD) were included. Participants with PTSD had higher body mass index (30.3 versus 29 kg/m; P < .05), higher ESS score (9.2 versus 7.6; P < .05), and increased alcohol or medication use to assist with sleep (19% versus 6%; P < .01; and 44% versus 14%; P < .01). Those with PTSD were less likely to sleep well (32% versus 72%; P < .01) and reported higher rates of restless legs (45% versus 25%; P < .01), nightmares (91% versus 29%; P < .01), nocturnal screaming (73% versus 18%; P < .01), sleep terrors (61% versus 13%; P < .01) and dream enactment (78% versus 11.8%; P < .01). The PTSD group had higher rates of diagnosed OSA (42% versus 21%; P < .01) and an increased risk of OSA on the Berlin Questionnaire (69% versus 43%; P < .01). Conclusions: Compared to trauma-exposed controls, Australian Vietnam veterans with PTSD demonstrated an increased prevalence of a wide range of sleep disturbances, including OSA. In veterans with PTSD, detailed sleep assessment, including consideration of polysomnography, is paramount

Detailed polysomnography in Australian Vietnam veterans with and without posttraumatic stress disorder

Study Objectives Recent results from the PTSD Initiative, a cross-sectional cohort study in Australian Vietnam veterans (VV) with and without posttraumatic stress disorder (PTSD), demonstrated an increased prevalence of self-reported sleep disturbances in those with PTSD. This study aimed to objectively assess the prevalence of sleep disorders in the same cohort using detailed polysomnography (PSG). Methods Participants from the PTSD Initiative were recruited to undergo PSG. PTSD status was determined with the Clinician Administered PTSD Scale for DSM-5 (CAPS-5). Subjective sleep information was attained via structured questionnaires. Data from single night PSG were compared between trauma-exposed VV with and without PTSD. Results A total of 74 trauma-exposed male VV (40 with PTSD) underwent PSG (prospective n = 59, retrospective n = 15). All PSG parameters were similar between groups. No difference was seen in PSG-diagnosed obstructive sleep apnea (OSA) or periodic limb movements of sleep (PLMS). VV with PTSD showed a trend toward increased duration of sleep with oxygen saturations < 90% (10% versus 1.8%; P = .07). VV with PTSD reported increased sleep onset latency (42.4 versus 13.3 minutes; P < .01); were less likely to report sleeping well (32.5% versus 67.5%; P < .01); had higher OSA risk using Berlin Questionnaire (BQ) (70% versus 38.2%; P < .01); and had higher rates of partner-reported limb movements (56.4% versus 17.6%; P < .01). No association between PSG-diagnosed OSA and PTSD severity was evident. Conclusions In Australian VV with and without PTSD, no difference was seen across all PSG parameters including the diagnosis and severity of OSA and PLMS. However, VV with PTSD demonstrated an increased perception of sleep disturbances

An International Comparison of Presentation, Outcomes and CORONET Predictive Score Performance in Patients with Cancer Presenting with COVID-19 across Different Pandemic Waves

SIMPLE SUMMARY: There have been huge improvements in both vaccination and the management of COVID-19 in patients with cancer. In addition, different variants may be associated with different presentations. Therefore, we examined whether indicators of the severity of COVID-19 in patients with cancer who present to hospital varied during different waves of the pandemic and we showed that these indicators remained predictive. We validated that the COVID-19 Risk in Oncology Evaluation Tool (CORONET), which predicts the severity of COVID-19 in cancer patients presenting to hospital, performed well in all waves. In addition, we examined patient outcomes and the factors that influence them and found that there was increased vaccination uptake and steroid use for patients requiring oxygen in later waves, which may be associated with improvements in outcome. ABSTRACT: Patients with cancer have been shown to have increased risk of COVID-19 severity. We previously built and validated the COVID-19 Risk in Oncology Evaluation Tool (CORONET) to predict the likely severity of COVID-19 in patients with active cancer who present to hospital. We assessed the differences in presentation and outcomes of patients with cancer and COVID-19, depending on the wave of the pandemic. We examined differences in features at presentation and outcomes in patients worldwide, depending on the waves of the pandemic: wave 1 D614G (n = 1430), wave 2 Alpha (n = 475), and wave 4 Omicron variant (n = 63, UK and Spain only). The performance of CORONET was evaluated on 258, 48, and 54 patients for each wave, respectively. We found that mortality rates were reduced in subsequent waves. The majority of patients were vaccinated in wave 4, and 94% were treated with steroids if they required oxygen. The stages of cancer and the median ages of patients significantly differed, but features associated with worse COVID-19 outcomes remained predictive and did not differ between waves. The CORONET tool performed well in all waves, with scores in an area under the curve (AUC) of >0.72. We concluded that patients with cancer who present to hospital with COVID-19 have similar features of severity, which remain discriminatory despite differences in variants and vaccination status. Survival improved following the first wave of the pandemic, which may be associated with vaccination and the increased steroid use in those patients requiring oxygen. The CORONET model demonstrated good performance, independent of the SARS-CoV-2 variants

Establishment of CORONET, COVID-19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID-19 Disease On Presentation to Hospital.

PurposePatients with cancer are at increased risk of severe COVID-19 disease, but have heterogeneous presentations and outcomes. Decision-making tools for hospital admission, severity prediction, and increased monitoring for early intervention are critical. We sought to identify features of COVID-19 disease in patients with cancer predicting severe disease and build a decision support online tool, COVID-19 Risk in Oncology Evaluation Tool (CORONET).MethodsPatients with active cancer (stage I-IV) and laboratory-confirmed COVID-19 disease presenting to hospitals worldwide were included. Discharge (within 24 hours), admission (≥ 24 hours inpatient), oxygen (O2) requirement, and death were combined in a 0-3 point severity scale. Association of features with outcomes were investigated using Lasso regression and Random Forest combined with Shapley Additive Explanations. The CORONET model was then examined in the entire cohort to build an online CORONET decision support tool. Admission and severe disease thresholds were established through pragmatically defined cost functions. Finally, the CORONET model was validated on an external cohort.ResultsThe model development data set comprised 920 patients, with median age 70 (range 5-99) years, 56% males, 44% females, and 81% solid versus 19% hematologic cancers. In derivation, Random Forest demonstrated superior performance over Lasso with lower mean squared error (0.801 v 0.807) and was selected for development. During validation (n = 282 patients), the performance of CORONET varied depending on the country cohort. CORONET cutoffs for admission and mortality of 1.0 and 2.3 were established. The CORONET decision support tool recommended admission for 95% of patients eventually requiring oxygen and 97% of those who died (94% and 98% in validation, respectively). The specificity for mortality prediction was 92% and 83% in derivation and validation, respectively. Shapley Additive Explanations revealed that National Early Warning Score 2, C-reactive protein, and albumin were the most important features contributing to COVID-19 severity prediction in patients with cancer at time of hospital presentation.ConclusionCORONET, a decision support tool validated in health care systems worldwide, can aid admission decisions and predict COVID-19 severity in patients with cancer

An International Comparison of Presentation, Outcomes and CORONET Predictive Score Performance in Patients with Cancer Presenting with COVID-19 across Different Pandemic Waves

Patients with cancer have been shown to have increased risk of COVID-19 severity. We previously built and validated the COVID-19 Risk in Oncology Evaluation Tool (CORONET) to predict the likely severity of COVID-19 in patients with active cancer who present to hospital. We assessed the differences in presentation and outcomes of patients with cancer and COVID-19, depending on the wave of the pandemic. We examined differences in features at presentation and outcomes in patients worldwide, depending on the waves of the pandemic: wave 1 D614G (n = 1430), wave 2 Alpha (n = 475), and wave 4 Omicron variant (n = 63, UK and Spain only). The performance of CORONET was evaluated on 258, 48, and 54 patients for each wave, respectively. We found that mortality rates were reduced in subsequent waves. The majority of patients were vaccinated in wave 4, and 94% were treated with steroids if they required oxygen. The stages of cancer and the median ages of patients significantly differed, but features associated with worse COVID-19 outcomes remained predictive and did not differ between waves. The CORONET tool performed well in all waves, with scores in an area under the curve (AUC) of >0.72. We concluded that patients with cancer who present to hospital with COVID-19 have similar features of severity, which remain discriminatory despite differences in variants and vaccination status. Survival improved following the first wave of the pandemic, which may be associated with vaccination and the increased steroid use in those patients requiring oxygen. The CORONET model demonstrated good performance, independent of the SARS-CoV-2 variants