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2.
PLoS One ; 17(1): e0262193, 2022.
Article in English | MEDLINE | ID: covidwho-1606289

ABSTRACT

OBJECTIVE: To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED). METHODS: We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict "severe" COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance. RESULTS: The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed "severe" COVID-19. Patients in the highest quintile developed "severe" COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05). CONCLUSION: A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.


Subject(s)
COVID-19/diagnosis , Decision Support Systems, Clinical , Logistic Models , Machine Learning , Triage/methods , COVID-19/physiopathology , Emergency Service, Hospital , Humans , ROC Curve , Severity of Illness Index
3.
J Bioeth Inq ; 18(4): 621-628, 2021 12.
Article in English | MEDLINE | ID: covidwho-1598709

ABSTRACT

The ever-debated question of triage and allocating the life-saving ventilator during the COVID-19 pandemic has been repeatedly raised and challenged within the ethical community after shortages propelled doctors before life and death decisions (Anderson-Shaw and Zar 2020; Huxtable 2020; Jongepier 2020; Peterson, Largent, and Karlawish 2020). The British Medical Association's ethical guidance highlighted the possibility of an initial surge of patients that would outstrip the health system's ability to deliver care "to existing standards," where utilitarian measures have to be applied, and triage decisions need to maximize "overall benefit" (British Medical Association 2020, 3) In these emergency circumstances, triage that "grades according to their needs and the probable outcomes of intervention" will prioritize or eliminate patients for treatment, and health professionals may be faced with obligations to withhold or withdraw treatments to some patients in favour of others (British Medical Association 2020, 4). This piece is a response and extension to articles published on the manner of involvement for ethics and ethicists in pandemic triage decisions, particularly examining the ability and necessity of establishing triage committees to ameliorate scarce allocation decisions for physicians.


Subject(s)
COVID-19 , Triage , Health Care Rationing , Humans , Pandemics , SARS-CoV-2
4.
J Med Internet Res ; 23(12): e25899, 2021 12 20.
Article in English | MEDLINE | ID: covidwho-1596879

ABSTRACT

BACKGROUND: The McIsaac criteria are a validated scoring system used to determine the likelihood of an acute sore throat being caused by group A streptococcus (GAS) to stratify patients who need strep testing. OBJECTIVE: We aim to compare McIsaac criteria obtained during face-to-face (f2f) and non-f2f encounters. METHODS: This retrospective study compared the percentage of positive GAS tests by McIsaac score for scores calculated during nurse protocol phone encounters, e-visits (electronic visits), and in person f2f clinic visits. RESULTS: There was no difference in percentages of positive strep tests between encounter types for any of the McIsaac scores. There were significantly more phone and e-visit encounters with any missing score components compared with f2f visits. For individual score components, there were significantly fewer e-visits missing fever and cough information compared with phone encounters and f2f encounters. F2f encounters were significantly less likely to be missing descriptions of tonsils and lymphadenopathy compared with phone and e-visit encounters. McIsaac scores of 4 had positive GAS rates of 55% to 68% across encounter types. There were 4 encounters not missing any score components with a McIsaac score of 0. None of these 4 encounters had a positive GAS test. CONCLUSIONS: McIsaac scores of 4 collected during non-f2f care could be used to consider empiric treatment for GAS without testing if significant barriers to testing exist such as the COVID-19 pandemic or geographic barriers. Future studies should evaluate further whether non-f2f encounters with McIsaac scores of 0 can be safely excluded from GAS testing.


Subject(s)
COVID-19 , Pharyngitis , Electronics , Humans , Outpatients , Pandemics , Pharyngitis/diagnosis , Retrospective Studies , SARS-CoV-2 , Triage
5.
Chron Respir Dis ; 18: 14799731211066507, 2021.
Article in English | MEDLINE | ID: covidwho-1582592

ABSTRACT

The COVID-19 pandemic has created new challenges for management of pleural diseases. As resources and staff have been redirected to manage acutely unwell COVID-19 patients, routine medical practice and service provision for pleural diseases have been severely disrupted. We recognised the impact this had for patients with pleural diseases, who can be highly vulnerable to infection and often have conditions for which treatment cannot be safely delayed. The pleural service was reviewed in a tertiary centre, focusing on the changes that allowed maintenance of a service whilst maximising patient and staff safety, with the aim that these service transformations can be adopted elsewhere to improve care for pleural patients during and beyond COVID-19.


Subject(s)
COVID-19 , Humans , Pandemics , SARS-CoV-2 , Triage
6.
BMJ Open Gastroenterol ; 8(1)2021 12.
Article in English | MEDLINE | ID: covidwho-1583129

ABSTRACT

OBJECTIVES: The COVID-19 pandemic significantly impacted on the provision of oesophageal physiology investigations. During the recovery phase, triaging tools were empirically recommended by national bodies for prioritisation of referrals amidst rising waiting lists and reduced capacity. We evaluated the performance of an enhanced triage process (ETP) consisting of telephone triage combined with the hierarchical 'traffic light system' recommended in the UK for prioritising oesophageal physiology referrals. DESIGN: In a cross-sectional study of patients referred for oesophageal physiology studies at a tertiary centre, data were compared between patients who underwent oesophageal physiology studies 6 months prior to the COVID-19 pandemic and those who were investigated within 6 months after service resumption with implementation of the ETP. OUTCOME MEASURES: Adjusted time from referral to investigation; non-attendance rates; the detection of Chicago Classification (CC) oesophageal motility disorders on oesophageal manometry and severity of acid reflux on 24 hours pH/impedance monitoring. RESULTS: Following service resumption, the ETP reduced non-attendance rates from 9.1% to 2.8% (p=0.021). Use of the 'traffic light system' identified a higher proportion of patients with CC oesophageal motility disorders in the 'amber' and 'red' triage categories, compared with the 'green' category (p=0.011). ETP also reduced the time to test for those who were subsequently found to have a major CC oesophageal motility diagnosis compared with those with minor CC disorders and normal motility (p=0.004). The ETP did not affect the yield or timing of acid reflux studies. CONCLUSION: ETPs can effectively prioritise patients with oesophageal motility disorders and may therefore have a role beyond the current pandemic.


Subject(s)
COVID-19 , Cross-Sectional Studies , Female , Humans , Pandemics , Pregnancy , SARS-CoV-2 , Triage
7.
BMJ Open Respir Res ; 8(1)2021 12.
Article in English | MEDLINE | ID: covidwho-1583084

ABSTRACT

INTRODUCTION: Global shortages in the supply of SARS-CoV-2 vaccines have resulted in campaigns to first inoculate individuals at highest risk for death from COVID-19. Here, we develop a predictive model of COVID-19-related death using longitudinal clinical data from patients in metropolitan Detroit. METHODS: All individuals included in the analysis had a laboratory-confirmed SARS-CoV-2 infection. Thirty-six pre-existing conditions with a false discovery rate p<0.05 were combined with other demographic variables to develop a parsimonious prediction model using least absolute shrinkage and selection operator regression. The model was then prospectively validated in a separate set of individuals with confirmed COVID-19. RESULTS: The study population consisted of 15 502 individuals with laboratory-confirmed SARS-CoV-2. The main prediction model was developed using data from 11 635 individuals with 709 reported deaths (case fatality ratio 6.1%). The final prediction model consisted of 14 variables with 11 comorbidities. This model was then prospectively assessed among the remaining 3867 individuals (185 deaths; case fatality ratio 4.8%). When compared with using an age threshold of 65 years, the 14-variable model detected 6% more of the individuals who would die from COVID-19. However, below age 45 years and its risk equivalent, there was no benefit to using the prediction model over age alone. DISCUSSION: Using a prediction model, such as the one described here, may help identify individuals who would most benefit from COVID-19 inoculation, and thereby may produce more dramatic initial drops in deaths through targeted vaccination.


Subject(s)
COVID-19 , Aged , COVID-19 Vaccines , Humans , Middle Aged , SARS-CoV-2 , Triage , Vaccination
8.
J Med Internet Res ; 23(2): e24246, 2021 02 10.
Article in English | MEDLINE | ID: covidwho-1573886

ABSTRACT

BACKGROUND: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease. OBJECTIVE: Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department. METHODS: Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics. RESULTS: The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics. CONCLUSIONS: The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.


Subject(s)
COVID-19/physiopathology , Hospitalization , Intubation, Intratracheal/statistics & numerical data , Machine Learning , Respiration, Artificial/statistics & numerical data , Respiratory Insufficiency/epidemiology , Aged , COVID-19/complications , Clinical Decision Rules , Early Warning Score , Emergency Service, Hospital , Female , Hospitals , Humans , Logistic Models , Male , Middle Aged , Patient Admission , ROC Curve , Respiratory Insufficiency/etiology , Retrospective Studies , SARS-CoV-2 , Triage
9.
J Glob Health ; 10(2): 0203103, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-1560513

Subject(s)
Internet , Software , Triage , Humans
12.
Lab Med ; 52(5): 493-498, 2021 Sep 01.
Article in English | MEDLINE | ID: covidwho-1526169

ABSTRACT

OBJECTIVE: The aim of the study was to assess the role of midregional proadrenomedullin (MR-proADM) in patients with COVID-19. METHODS: We included 110 patients hospitalized for COVID-19. Biochemical biomarkers, including MR-proADM, were measured at admission. The association of plasma MR-proADM levels with COVID-19 severity, defined as a requirement for mechanical ventilation or in-hospital mortality, was evaluated. RESULTS: Patients showed increased levels of MR-proADM. In addition, MR-proADM was higher in patients who died during hospitalization than in patients who survived (median, 2.59 nmol/L; interquartile range, 2.3-2.95 vs median, 0.82 nmol/L; interquartile range, 0.57-1.03; P <.0001). Receiver operating characteristic curve analysis showed good accuracy of MR-proADM for predicting mortality. A MR-proADM value of 1.73 nmol/L was established as the best cutoff value, with 90% sensitivity and 95% specificity (P <.0001). CONCLUSION: We found that MR-proADM could represent a prognostic biomarker of COVID-19.


Subject(s)
Adrenomedullin/blood , COVID-19/diagnosis , Hypertension/diagnosis , Lung Diseases/diagnosis , Protein Precursors/blood , Aged , Alanine Transaminase/blood , Aspartate Aminotransferases/blood , Biomarkers/blood , C-Reactive Protein/metabolism , COVID-19/blood , COVID-19/mortality , COVID-19/virology , Comorbidity , Female , Humans , Hypertension/blood , Hypertension/mortality , Hypertension/virology , Interleukin-6/blood , Lung Diseases/blood , Lung Diseases/mortality , Lung Diseases/virology , Male , Middle Aged , Patient Selection , Prognosis , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index , Survival Analysis , Triage/methods
13.
JCO Clin Cancer Inform ; 5: 1134-1140, 2021 10.
Article in English | MEDLINE | ID: covidwho-1518337

ABSTRACT

PURPOSE: Patients with cancer are at greater risk of developing severe symptoms from COVID-19 than the general population. We developed and tested an automated text-based remote symptom-monitoring program to facilitate early detection of worsening symptoms and rapid assessment for patients with cancer and suspected or confirmed COVID-19. METHODS: We conducted a feasibility study of Cancer COVID Watch, an automated COVID-19 symptom-monitoring program with oncology nurse practitioner (NP)-led triage among patients with cancer between April 23 and June 30, 2020. Twenty-six patients with cancer and suspected or confirmed COVID-19 were enrolled. Enrolled patients received twice daily automated text messages over 14 days that asked "How are you feeling compared to 12 hours ago? Better, worse, or the same?" and, if worse, "Is it harder than usual for you to breathe?" Patients who responded worse and yes were contacted within 1 hour by an oncology NP. RESULTS: Mean age of patients was 62.5 years. Seventeen (65%) were female, 10 (38%) Black, and 15 (58%) White. Twenty-five (96%) patients responded to ≥ 1 symptom check-in, and overall response rate was 78%. Four (15%) patients were escalated to the triage line: one was advised to present to the emergency department (ED), and three were managed in the outpatient setting. Median time from escalation to triage call was 11.5 minutes. Four (15%) patients presented to the ED without first escalating their care via our program. Participant satisfaction was high (Net Promoter Score: 100, n = 4). CONCLUSION: Implementation of an intensive remote symptom monitoring and rapid NP triage program for outpatients with cancer and suspected or confirmed COVID-19 infection is possible. Similar tools may facilitate more rapid triage for patients with cancer in future pandemics.


Subject(s)
COVID-19 , Neoplasms , Text Messaging , Female , Humans , Middle Aged , Neoplasms/diagnosis , SARS-CoV-2 , Triage
14.
J Radiol Prot ; 41(4)2021 11 15.
Article in English | MEDLINE | ID: covidwho-1517769

ABSTRACT

A collection of powerful diagnostic tools have been developed under the umbrellas of NATO for ionising radiation dose assessment (BAT, WinFRAT) and estimate of acute health effects in humans (WinFRAT, H-Module). We assembled a database of 191 ARS cases using the medical treatment protocols for radiation accident victims (n= 167) and the system for evaluation and archiving of radiation accidents based on case histories (n= 24) for training purposes of medical personnel. From 2016 to 2019, we trained 39 participants comprising MSc level radiobiology students in an on-site teaching class. Enforced by the covid-19 pandemic in 2020 for the first time, an online teaching of nine MSc radiobiology students replaced the on-site teaching. We found that: (a) limitations of correct diagnostic decision-making based on clinical signs and symptoms were experienced unrelated to the teaching format. (b) A significant performance decrease concerning online (first number in parenthesis) versus on-site teaching (reference and second number in parenthesis) was seen regarding the estimate time (31 vs 61 cases per hour, two-fold decrease,p= 0.005). Also, the accurate assessment of response categories (89.9% vs 96.9%,p= 0.001), ARS (92.4% vs 96.7%,p= 0.002) and hospitalisation (93.5% vs 97.0%,p= 0.002) decreased by around 3%-7%. The performances of the online attendees were mainly distributed within the lower quartile performance of on-site participants and the 25%-75% interquartile range increased 3-7-fold. (c) Comparison of dose estimates performed by training participants with hematologic acute radiation syndrome (HARS) severity mirrored the known limitations of dose alone as a surrogate parameter for HARS severity at doses less than 1.5 Gy, but demonstrated correct determination of HARS 2-4 and support for clinical decision making at dose estimates >1.5 Gy, regardless of teaching format. (d) Overall, one-third of the online participants showed substantial misapprehension and insecurities of elementary course content that did not occur after the on-site teaching.


Subject(s)
Acute Radiation Syndrome , COVID-19 , Humans , Pandemics , SARS-CoV-2 , Triage
15.
Surg Clin North Am ; 102(1): 169-180, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1517479

ABSTRACT

Mass casualty incidents are increasingly common. They are defined by large numbers of patients arriving nearly simultaneously, overwhelming available resources needed for optimal care. They require rapid mobilization of resources to provide optimal outcomes and limit disability and death. Because the mechanism of injury in a mass casualty incident is often traumatic in nature, surgeons should be aware of the critical role they play in planning and response. The coronavirus disease 2019 pandemic is a notable, resulting in a sustained surge of critically ill patients. Initial response requires local mobilization of resources; large-scale events potentially require a national response.


Subject(s)
Civil Defense , Emergency Medical Services , Health Resources , Mass Casualty Incidents , COVID-19/epidemiology , COVID-19/prevention & control , Decision Trees , Humans , Triage
16.
Minerva Pediatr (Torino) ; 73(5): 460-466, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1513377

ABSTRACT

Inevitably, along with other healthcare specializations, pediatric surgery was affected by the Coronavirus disease-19 (COVID-19) pandemic. Children were reported to manifest mild to moderate symptoms and mortality was primarily observed in patients aged <1 year and having underlying comorbidities. Most of the cases were asymptomatic in children, hence, posing a challenge for pediatric surgery centers to take drastic measures to reduce the virus transmission. Telemedicine was introduced and out-patient consultations were conducted online as out-patient clinics were closed. Elective surgeries were postponed with delayed appointments while the healthcare sector was diverted towards tackling COVID-19. Case urgency was classified and triaged, leading to limited surgeries being performed only in COVID-19 negative patients following an extensive screening process. The screening process consisted of online history taking and RT-PCR tests. Newer practices such as mouth rinse, video laryngoscopy, and anesthesia were introduced to restrict patients from crying, coughing, and sneezing, as an attempt to avoid aerosolization of viral particles and safely conduct pediatric surgeries during the pandemic. Surgical trainees were also affected as the smaller number of surgeries conducted reduced the clinical experience available to medical enthusiasts. There is still room for advanced practices to be introduced in pediatric surgery and restore all kinds of surgeries to improve the quality of life of the patient.


Subject(s)
COVID-19/epidemiology , COVID-19/prevention & control , Pandemics , Pediatrics , Surgical Procedures, Operative , Asymptomatic Infections/epidemiology , COVID-19/diagnosis , COVID-19 Nucleic Acid Testing/methods , Child , Child, Preschool , Elective Surgical Procedures , General Surgery/education , Humans , Incidence , Infant , Patient Selection , Pediatrics/education , Preoperative Care/methods , Surgical Procedures, Operative/education , Telemedicine/organization & administration , Triage
17.
Pan Afr Med J ; 40: 41, 2021.
Article in English | MEDLINE | ID: covidwho-1513182

ABSTRACT

Introduction: the coronavirus disease 2019 (COVID-19) pandemic has negatively impacted countries across the globe. Infected individuals will seek aid at various health care facilities. Many patients will recover without requiring specialised treatment. A significant percentage of infected individuals will need critical care management, which will begin in the emergency department, generally staffed by junior doctors. Junior doctors will need to stabilize, triage and manage these patients prior to referral to specialized units. Above and beyond the usual occupational demands that accompany junior doctors in state facilities, this pandemic will thrust further responsibility on them. The objectives were to describe crisis preparedness of junior doctors in the areas of triage decision-making and critical care management, outside the intensive care unit. Methods: this is a descriptive, cross-sectional study, utilizing a web-based survey. Junior doctors in South Africa, being doctors in year one or year two of internship and community service, were invited to participate anonymously via various social media platforms. Results: a total of 210 junior doctors across South Africa answered the survey. Junior doctors expressed confidence with knowledge of intubation drugs, to perform intubation and cardiopulmonary arrest resuscitation without supervision. Only 13.3% of respondents expressed comfort with setting and adjusting ventilator settings independently. 57% of participants expressed discomfort with making critical care triage decisions. Ninety-three percent (93%) of participants expressed benefit from a telemedicine intervention. Conclusion: junior doctors in South Africa indicate that they are prepared to initiate management of the critically ill patient outside the intensive care unit but remain uncertain in their ability to provide ongoing critical care management. The COVID-19 pandemic has highlighted the need to prepare junior doctors with the ability to manage critical care triage and management in emergency rooms. Leveraging of the workforce in South Africa may be potentiated by telemedicine interventions.


Subject(s)
COVID-19 , Critical Care/methods , Medical Staff, Hospital/statistics & numerical data , Triage/methods , Clinical Competence , Clinical Decision-Making , Critical Illness/therapy , Cross-Sectional Studies , Emergency Service, Hospital/organization & administration , Humans , Intensive Care Units , Internship and Residency , South Africa , Surveys and Questionnaires
19.
Sci Rep ; 11(1): 21923, 2021 11 09.
Article in English | MEDLINE | ID: covidwho-1510619

ABSTRACT

We developed a tool to guide decision-making for early triage of COVID-19 patients based on a predicted prognosis, using a Korean national cohort of 5,596 patients, and validated the developed tool with an external cohort of 445 patients treated in a single institution. Predictors chosen for our model were older age, male sex, subjective fever, dyspnea, altered consciousness, temperature ≥ 37.5 °C, heart rate ≥ 100 bpm, systolic blood pressure ≥ 160 mmHg, diabetes mellitus, heart disease, chronic kidney disease, cancer, dementia, anemia, leukocytosis, lymphocytopenia, and thrombocytopenia. In the external validation, when age, sex, symptoms, and underlying disease were used as predictors, the AUC used as an evaluation metric for our model's performance was 0.850 in predicting whether a patient will require at least oxygen therapy and 0.833 in predicting whether a patient will need critical care or die from COVID-19. The AUCs improved to 0.871 and 0.864, respectively, when additional information on vital signs and blood test results were also used. In contrast, the protocols currently recommended in Korea showed AUCs less than 0.75. An application for calculating the prognostic score in COVID-19 patients based on the results of this study is presented on our website ( https://nhimc.shinyapps.io/ih-psc/ ), where the results of the validation ongoing in our institution are periodically updated.


Subject(s)
COVID-19 , Humans , Middle Aged , Prognosis , Triage
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