Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 10 de 10
Filter
1.
Clin Chest Med ; 43(3): 551-561, 2022 09.
Article in English | MEDLINE | ID: covidwho-2060493

ABSTRACT

Improvements in critical care medicine have led to a marked increase in survivors of the intensive care unit (ICU). These survivors encounter many difficulties following ICU discharge. The term post -intensive care syndrome (PICS) provides a framework for identifying the most common symptoms which fall into three domains: cognitive, physical, and mental health. There are numerous risk factors for the development of PICS including premorbid conditions and specific elements of ICU hospitalizations. Management is complex and should take an individualized approach with interdisciplinary care. Future research should focus on prevention, identification, and treatment of this unique population.


Subject(s)
Critical Illness , Survivorship , Critical Care , Critical Illness/psychology , Critical Illness/therapy , Humans , Intensive Care Units
2.
Critical care explorations ; 4(3), 2022.
Article in English | EuropePMC | ID: covidwho-1738096

ABSTRACT

OBJECTIVES: The multifaceted long-term impairments resulting from critical illness and COVID-19 require interdisciplinary management approaches in the recovery phase of illness. Operational insights into the structure and process of recovery clinics (RCs) from heterogeneous health systems are needed. This study describes the structure and process characteristics of existing and newly implemented ICU-RCs and COVID-RCs in a subset of large health systems in the United States. DESIGN: Cross-sectional survey. SETTING: Thirty-nine RCs, representing a combined 156 hospitals within 29 health systems participated. PATIENTS: None. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: RC demographics, referral criteria, and operating characteristics were collected, including measures used to assess physical, psychologic, and cognitive recoveries. Thirty-nine RC surveys were completed (94% response rate). ICU-RC teams included physicians, pharmacists, social workers, physical therapists, and advanced practice providers. Funding sources for ICU-RCs included clinical billing (n = 20, 77%), volunteer staff support (n = 15, 58%), institutional staff/space support (n = 13, 46%), and grant or foundation funding (n = 3, 12%). Forty-six percent of RCs report patient visit durations of 1 hour or longer. ICU-RC teams reported use of validated scales to assess psychologic recovery (93%), physical recovery (89%), and cognitive recovery (86%) more often in standard visits compared with COVID-RC teams (psychologic, 54%;physical, 69%;and cognitive, 46%). CONCLUSIONS: Operating structures of RCs vary, though almost all describe modest capacity and reliance on volunteerism and discretionary institutional support. ICU- and COVID-RCs in the United States employ varied funding sources and endorse different assessment measures during visits to guide care coordination. Common features include integration of ICU clinicians, interdisciplinary approach, and focus on severe critical illness. The heterogeneity in RC structures and processes contributes to future research on the optimal structure and process to achieve the best postintensive care syndrome and postacute sequelae of COVID outcomes.

3.
4.
ATS Sch ; 2(3): 452-467, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1478979

ABSTRACT

The following is a concise review of the Pediatric Pulmonary Medicine Core reviewing pediatric pulmonary infections, diagnostic assays, and imaging techniques presented at the 2021 American Thoracic Society Core Curriculum. Molecular methods have revolutionized microbiology. We highlight the need to collect appropriate samples for detection of specific pathogens or for panels and understand the limitations of the assays. Considerable progress has been made in imaging modalities for detecting pediatric pulmonary infections. Specifically, lung ultrasound and lung magnetic resonance imaging are promising radiation-free diagnostic tools, with results comparable with their radiation-exposing counterparts, for the evaluation and management of pulmonary infections. Clinicians caring for children with pulmonary disease should ensure that patients at risk for nontuberculous mycobacteria disease are identified and receive appropriate nontuberculous mycobacteria screening, monitoring, and treatment. Children with coronavirus disease (COVID-19) typically present with mild symptoms, but some may develop severe disease. Treatment is mainly supportive care, and most patients make a full recovery. Anticipatory guidance and appropriate counseling from pediatricians on social distancing and diagnostic testing remain vital to curbing the pandemic. The pediatric immunocompromised patient is at risk for invasive and opportunistic pulmonary infections. Prompt recognition of predisposing risk factors, combined with knowledge of clinical characteristics of microbial pathogens, can assist in the diagnosis and treatment of specific bacterial, viral, or fungal diseases.

5.
Medicine (Baltimore) ; 100(37): e27265, 2021 Sep 17.
Article in English | MEDLINE | ID: covidwho-1434547

ABSTRACT

ABSTRACT: During the spring 2020 COVID-19 surge, hospitals in Southeast Michigan were overwhelmed, and hospital beds were limited. However, it is unknown whether threshold for hospital admission varied across hospitals or over time.Using a statewide registry, we performed a retrospective cohort study. We identified adult patients hospitalized with COVID-19 in Southeast Michigan (3/1/2020-6/1/2020). We classified disease severity on admission using the World Health Organization (WHO) ordinal scale. Our primary measure of interest was the proportion of patients admitted on room air. We also determined the proportion without acute organ dysfunction on admission or any point during hospitalization. We quantified variation across hospitals and over time by half-month epochs.Among 1315 hospitalizations across 22 hospitals, 57.3% (754/1,315) were admitted on room air, and 26.1% (343/1,315) remained on room air for the duration of hospitalization. Across hospitals, the proportion of COVID-19 hospitalizations admitted on room air varied from 32.3% to 80.0%. Across half-month epochs, the proportion ranged from 49.4% to 69.4% and nadired in early April 2020. Among patients admitted on room air, 75.1% (566/754) had no acute organ dysfunction on admission, and 35.3% (266/754) never developed acute organ dysfunction at any point during hospitalization; there was marked variation in both proportions across hospitals. In-hospital mortality was 13.7% for patients admitted on room air vs 26.3% for patients requiring nasal cannula oxygen.Among patients hospitalized with COVID-19 during the spring 2020 surge in Southeast Michigan, more than half were on room air and a third had no acute organ dysfunction upon admission, but experienced high rates of disease progression and in-hospital mortality.


Subject(s)
COVID-19/complications , Hospitalization/statistics & numerical data , Aged , Cohort Studies , Female , Humans , Male , Michigan , Middle Aged , Severity of Illness Index , Time Factors
6.
JMIR Med Inform ; 9(4): e25066, 2021 Apr 21.
Article in English | MEDLINE | ID: covidwho-1200031

ABSTRACT

BACKGROUND: COVID-19 has led to an unprecedented strain on health care facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here, we present the results of an analytical model, Predicting Intensive Care Transfers and Other Unforeseen Events (PICTURE), to identify patients at high risk for imminent intensive care unit transfer, respiratory failure, or death, with the intention to improve the prediction of deterioration due to COVID-19. OBJECTIVE: This study aims to validate the PICTURE model's ability to predict unexpected deterioration in general ward and COVID-19 patients, and to compare its performance with the Epic Deterioration Index (EDI), an existing model that has recently been assessed for use in patients with COVID-19. METHODS: The PICTURE model was trained and validated on a cohort of hospitalized non-COVID-19 patients using electronic health record data from 2014 to 2018. It was then applied to two holdout test sets: non-COVID-19 patients from 2019 and patients testing positive for COVID-19 in 2020. PICTURE results were aligned to EDI and NEWS scores for head-to-head comparison via area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve. We compared the models' ability to predict an adverse event (defined as intensive care unit transfer, mechanical ventilation use, or death). Shapley values were used to provide explanations for PICTURE predictions. RESULTS: In non-COVID-19 general ward patients, PICTURE achieved an AUROC of 0.819 (95% CI 0.805-0.834) per observation, compared to the EDI's AUROC of 0.763 (95% CI 0.746-0.781; n=21,740; P<.001). In patients testing positive for COVID-19, PICTURE again outperformed the EDI with an AUROC of 0.849 (95% CI 0.820-0.878) compared to the EDI's AUROC of 0.803 (95% CI 0.772-0.838; n=607; P<.001). The most important variables influencing PICTURE predictions in the COVID-19 cohort were a rapid respiratory rate, a high level of oxygen support, low oxygen saturation, and impaired mental status (Glasgow Coma Scale). CONCLUSIONS: The PICTURE model is more accurate in predicting adverse patient outcomes for both general ward patients and COVID-19 positive patients in our cohorts compared to the EDI. The ability to consistently anticipate these events may be especially valuable when considering potential incipient waves of COVID-19 infections. The generalizability of the model will require testing in other health care systems for validation.

7.
Lancet Digit Health ; 3(6): e340-e348, 2021 06.
Article in English | MEDLINE | ID: covidwho-1193002

ABSTRACT

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a common, but under-recognised, critical illness syndrome associated with high mortality. An important factor in its under-recognition is the variability in chest radiograph interpretation for ARDS. We sought to train a deep convolutional neural network (CNN) to detect ARDS findings on chest radiographs. METHODS: CNNs were pretrained on 595 506 radiographs from two centres to identify common chest findings (eg, opacity and effusion), and then trained on 8072 radiographs annotated for ARDS by multiple physicians using various transfer learning approaches. The best performing CNN was tested on chest radiographs in an internal and external cohort, including a subset reviewed by six physicians, including a chest radiologist and physicians trained in intensive care medicine. Chest radiograph data were acquired from four US hospitals. FINDINGS: In an internal test set of 1560 chest radiographs from 455 patients with acute hypoxaemic respiratory failure, a CNN could detect ARDS with an area under the receiver operator characteristics curve (AUROC) of 0·92 (95% CI 0·89-0·94). In the subgroup of 413 images reviewed by at least six physicians, its AUROC was 0·93 (95% CI 0·88-0·96), sensitivity 83·0% (95% CI 74·0-91·1), and specificity 88·3% (95% CI 83·1-92·8). Among images with zero of six ARDS annotations (n=155), the median CNN probability was 11%, with six (4%) assigned a probability above 50%. Among images with six of six ARDS annotations (n=27), the median CNN probability was 91%, with two (7%) assigned a probability below 50%. In an external cohort of 958 chest radiographs from 431 patients with sepsis, the AUROC was 0·88 (95% CI 0·85-0·91). When radiographs annotated as equivocal were excluded, the AUROC was 0·93 (0·92-0·95). INTERPRETATION: A CNN can be trained to achieve expert physician-level performance in ARDS detection on chest radiographs. Further research is needed to evaluate the use of these algorithms to support real-time identification of ARDS patients to ensure fidelity with evidence-based care or to support ongoing ARDS research. FUNDING: National Institutes of Health, Department of Defense, and Department of Veterans Affairs.


Subject(s)
Deep Learning , Neural Networks, Computer , Radiographic Image Interpretation, Computer-Assisted/methods , Radiography, Thoracic , Respiratory Distress Syndrome/diagnosis , Aged , Algorithms , Area Under Curve , Datasets as Topic , Female , Hospitals , Humans , Lung/diagnostic imaging , Lung/pathology , Male , Middle Aged , Pleural Cavity/diagnostic imaging , Pleural Cavity/pathology , Pleural Diseases , Radiography , Respiratory Distress Syndrome/diagnostic imaging , Retrospective Studies , United States
8.
Ann Am Thorac Soc ; 18(11): 1876-1885, 2021 11.
Article in English | MEDLINE | ID: covidwho-1084007

ABSTRACT

Rationale: Patients with severe coronavirus disease (COVID-19) meet clinical criteria for the acute respiratory distress syndrome (ARDS), yet early reports suggested they differ physiologically and clinically from patients with non-COVID-19 ARDS, prompting treatment recommendations that deviate from standard evidence-based practices for ARDS. Objectives: To compare respiratory physiology, clinical outcomes, and extrapulmonary clinical features of severe COVID-19 with non-COVID-19 ARDS. Methods: We performed a retrospective cohort study, comparing 130 consecutive mechanically ventilated patients with severe COVID-19 with 382 consecutive mechanically ventilated patients with non-COVID-19 ARDS. Initial respiratory physiology and 28-day outcomes were compared. Extrapulmonary manifestations (inflammation, extrapulmonary organ injury, and coagulation) were compared in an exploratory analysis. Results: Comparison of patients with COVID-19 and non-COVID-19 ARDS suggested small differences in respiratory compliance, ventilatory efficiency, and oxygenation. The 28-day mortality was 30% in patients with COVID-19 and 38% in patients with non-COVID-19 ARDS. In adjusted analysis, point estimates of differences in time to breathing unassisted at 28 days (adjusted subdistributional hazards ratio, 0.98 [95% confidence interval (CI), 0.77-1.26]) and 28-day mortality (risk ratio, 1.01 [95% CI, 0.72-1.42]) were small for COVID-19 versus non-COVID-19 ARDS, although the confidence intervals for these estimates include moderate differences. Patients with COVID-19 had lower neutrophil counts but did not differ in lymphocyte count or other measures of systemic inflammation. Conclusions: In this single-center cohort, we found no evidence for large differences between COVID-19 and non-COVID-19 ARDS. Many key clinical features of severe COVID-19 were similar to those of non-COVID-19 ARDS, including respiratory physiology and clinical outcomes, although our sample size precludes definitive conclusions. Further studies are needed to define COVID-19-specific pathophysiology before a deviation from evidence-based treatment practices can be recommended.


Subject(s)
COVID-19 , Respiratory Distress Syndrome , Humans , Respiration, Artificial , Respiratory Distress Syndrome/therapy , Retrospective Studies , SARS-CoV-2
9.
Crit Care Explor ; 2(12): e0303, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-998495

ABSTRACT

OBJECTIVES: To characterize the incidence and characteristics of propofol-associated hypertriglyceridemia in coronavirus disease 2019 versus noncoronavirus disease 2019 acute respiratory distress syndrome. DESIGN: Single-center prospective, observational cohort study. SETTING: Medical ICU and regional infectious containment unit. PATIENTS: Patients with acute respiratory distress syndrome admitted from April 7, 2020, to May 15, 2020, requiring continuous propofol administration. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 50 patients enrolled, 54% had coronavirus disease 2019 acute respiratory distress syndrome. Median Acute Physiology and Chronic Health Evaluation II and Sequential Organ Failure Assessment scores were 35.5 (interquartile range, 30.2-41) and 8 (interquartile range, 6-9). Pao2/Fio2 ratio was 130.5 (interquartile range, 94.5-193.8). Patients with coronavirus disease 2019-associated acute respiratory distress syndrome experienced a higher rate of hypertriglyceridemia (triglyceride ≥ 500 mg/dL) than noncoronavirus disease 2019-associated acute respiratory distress syndrome (9 [33.3%] vs 1 [4.3%]; p = 0.014). Those with coronavirus disease 2019, compared with those without, received more propofol prior to becoming hypertriglyceridemic (median, 5,436.0 mg [interquartile range, 3,405.5-6,845.5 mg] vs 4,229.0 mg [interquartile range, 2,083.4-4,972.1 mg]; p = 0.027). After adjustment for propofol dose with logistic regression (odds ratio, 5.97; 95% CI, 1.16-59.57; p = 0.031) and propensity score matching (odds ratio, 8.64; 95% CI, 1.27-149.12; p = 0.025), there remained a significant difference in the development of hypertriglyceridemia between coronavirus disease 2019-associated acute respiratory distress syndrome and noncoronavirus disease 2019-associated acute respiratory distress syndrome. There was no difference between groups in time to hypertriglyceridemia (p = 0.063). Serum lipase was not different between those who did or did not develop hypertriglyceridemia (p = 0.545). No patients experienced signs or symptoms of pancreatitis. CONCLUSIONS: Patients with coronavirus disease 2019 acute respiratory distress syndrome experienced a higher rate of propofol-associated hypertriglyceridemia than noncoronavirus disease 2019 acute respiratory distress syndrome patients, even after accounting for differences in propofol administration.

SELECTION OF CITATIONS
SEARCH DETAIL