scholarly journals Performance of TOTAL, in medical patients attending a resource-poor hospital in sub-Saharan Africa and a small Irish rural hospital

2013 ◽  
Vol 12 (3) ◽  
pp. 135-140
Author(s):  
Martin Otyek Opio ◽  
◽  
Gertrude Nansubuga ◽  
John Kellett ◽  
M Cliffor ◽  
...  
Keyword(s):  
Characteristic Curve ◽  
Altered Mental Status ◽  
Receiver Operator Characteristic Curve ◽  
Rural Hospital ◽  
Sub Saharan Africa ◽  
Early Warning Score ◽  
Medical Patients ◽  
Resource Poor ◽  
Hospital Patients ◽  
African Patients

Background: Recently a very simple, easy to remember early warning score (EWS) dubbed TOTAL has been reported. The score was derived from 309 acutely ill medical patients admitted to a Malawian hospital and awards one point for Tachypnea >30 breaths per minute, one point for Oxygen saturation <90%, two points for a Temperature <35°C, one point for Altered mental status, and one point for Loss of independence as indicated by the inability to stand or walk without help. TOTAL has an area under the receiver operator characteristic curve (AUROC) for death within 72 hours of 78%. Methods: We compared the performance of the TOTAL score in 849 medical patients attending a resource poor hospital in Uganda and 2935 patients admitted to a small rural hospital in Ireland. Results: TOTAL’s AUROC for death within 24 hours was the same in both hospital populations: 85.1% (95% CI 78.6 – 91.6%) for Kitovu Hospital patients and 84.7% (95% CI 77.1 – 92.2%) for Nenagh Hospital patients. Conclusion: The discrimination of TOTAL is exactly the same in elderly Irish patients as it is in young African patients. The score is easy to remember, easy to calculate, and works over a broad range of patients.

scholarly journals Respiratory rates observed over 15 and 30 s compared with rates measured over 60 s: practice-based evidence from an observational study of acutely ill adult medical patients during hospital admission

QJM ◽  
2019 ◽  
Vol 112 (7) ◽  
pp. 513-517 ◽  
Author(s):  
M Rimbi ◽  
D Dunsmuir ◽  
J M Ansermino ◽  
I Nakitende ◽  
T Namujwiga ◽  
...  
Keyword(s):  
Observational Study ◽  
Respiratory Rate ◽  
Sub Saharan Africa ◽  
Early Warning Score ◽  
Medical Patients ◽  
Rate Measurement ◽  
Limits Of Agreement ◽  
Resource Poor ◽  
Sub Saharan ◽  
Single Center Observational Study

AbstractBackgroundRespiratory rate is often measured over a period shorter than 1 min and then multiplied to produce a rate per minute. There are few reports of the performance of such estimates compared with rates measured over a full minute.AimCompare performance of respiratory rates calculated from 15 and 30 s of observations with measurements over 1 min.DesignA prospective single center observational studyMethodsThe respiratory rates calculated from observations for 15 and 30 s were compared with simultaneous respiratory rates measured for a full minute on acutely ill medical patients during their admission to a resource poor hospital in sub-Saharan Africa using a novel respiratory rate tap counting software app.ResultsThere were 770 respiratory rates recorded on 321 patients while they were in the hospital. The bias (limits of agreement) between the rate derived from 15 s of observations and the full minute was −1.22 breaths per minute (bpm) (−7.16 to 4.72 bpm), and between the rate derived from 30 s and the full minute was −0.46 bpm (–3.89 to 2.97 bpm). Rates observed over 1 min that scored 3 National Early Warning Score points were not identified by half the rates derived from 15 s and a quarter of the rates derived from 30 s.ConclusionPractice-based evidence shows that abnormal respiratory rates are more reliably detected with measurements made over a full minute, and respiratory rate measurement ‘short-cuts’ often fail to identify sick patients.

scholarly journals A new approach to scoring systems to improve identification of acute medical admissions that will require critical care

2011 ◽  
Vol 56 (4) ◽  
pp. 195-202 ◽  
Author(s):  
H A Carmichael ◽  
E Robertson ◽  
J Austin ◽  
D Mccruden ◽  
C M Messow ◽  
...  
Keyword(s):  
Early Warning ◽  
Characteristic Curve ◽  
Medical Patient ◽  
Scoring Systems ◽  
Receiver Operator Characteristic Curve ◽  
Early Warning Score ◽  
Peripheral Oxygen Saturation ◽  
Logistic Regression Models ◽  
Prospective Application ◽  
Gas Measurements

Removal of the intensive care unit (ICU) at the Vale of Leven Hospital mandated the identification and transfer out of those acute medical admissions with a high risk of requiring ICU. The aim of the study was to develop triaging tools that identified such patients and compare them with other scoring systems. The methodology included a retrospective analysis of physiological and arterial gas measurements from 1976 acute medical admissions produced PREEMPT-1 (PRE-critical Emergency Medical Patient Triage). A simpler one for ambulance use (PREAMBLE-1 [PRE-Admission Medical Blue-Light Emergency]) was produced by the addition of peripheral oxygen saturation to a modification of MEWS (Modified Early Warning Score). Prospective application of these tools produced a larger database of 4447 acute admissions from which logistic regression models produced PREEMPT-2 and PREAMBLE-2, which were then compared with the original systems and seven other early warning scoring systems. Results showed that in patients with arterial gases, the area under the receiver operator characteristic curve was significantly higher in PREEMPT-2 (89·1%) and PREAMBLE-2 (84.4%) than all other scoring systems. Similarly, in all patients, it was higher in PREAMBLE-2 (92.4%) than PREAMBLE-1 (88.1%) and the other scoring systems. In conclusion, risk of requiring ICU can be more accurately predicted using PREEMPT-2 and PREAMBLE-2, as described here, than by other early warning scoring systems developed over recent years.

scholarly journals Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Viktor Tóth ◽  
Marsha Meytlis ◽  
Douglas P. Barnaby ◽  
Kevin R. Bock ◽  
Michael I. Oppenheim ◽  
...  
Keyword(s):  
Vital Sign ◽  
Characteristic Curve ◽  
Healthcare Providers ◽  
Vital Signs ◽  
Early Warning Score ◽  
Hospital Patients ◽  
Small Set ◽  
Using Data ◽  
Impaired Sleep ◽  
Deep Learning Model

AbstractImpaired sleep for hospital patients is an all too common reality. Sleep disruptions due to unnecessary overnight vital sign monitoring are associated with delirium, cognitive impairment, weakened immunity, hypertension, increased stress, and mortality. It is also one of the most common complaints of hospital patients while imposing additional burdens on healthcare providers. Previous efforts to forgo overnight vital sign measurements and improve patient sleep used providers’ subjective stability assessment or utilized an expanded, thus harder to retrieve, set of vitals and laboratory results to predict overnight clinical risk. Here, we present a model that incorporates past values of a small set of vital signs and predicts overnight stability for any given patient-night. Using data obtained from a multi-hospital health system between 2012 and 2019, a recurrent deep neural network was trained and evaluated using ~2.3 million admissions and 26 million vital sign assessments. The algorithm is agnostic to patient location, condition, and demographics, and relies only on sequences of five vital sign measurements, a calculated Modified Early Warning Score, and patient age. We achieved an area under the receiver operating characteristic curve of 0.966 (95% confidence interval [CI] 0.956–0.967) on the retrospective testing set, and 0.971 (95% CI 0.965–0.974) on the prospective set to predict overnight patient stability. The model enables safe avoidance of overnight monitoring for ~50% of patient-nights, while only misclassifying 2 out of 10,000 patient-nights as stable. Our approach is straightforward to deploy, only requires regularly obtained vital signs, and delivers easily actionable clinical predictions for a peaceful sleep in hospitals.

scholarly journals Comparison of different prognostic scores for risk stratification in septic patients arriving to the Emergency Department

2020 ◽  
Vol 14 (2) ◽  
pp. 79-87
Author(s):  
Valeria Caramello ◽  
Valentina Beux ◽  
Alessandro Vincenzo De Salve ◽  
Alessandra Macciotta ◽  
Fulvio Ricceri ◽  
...  
Keyword(s):  
Emergency Department ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Prognostic Scores ◽  
Early Warning Score ◽  
Prognostic Performance ◽  
Icu Admission ◽  
Risk Of Mortality ◽  
Failure Assessment ◽  
Patients At Risk

We evaluated the prognostic performance of systemic inflammatory response syndrome (SIRS), sequential organ failure assessment (SOFA), quick-SOFA (qSOFA), modified early warning score (MEWS), lactates and procalcitonin in septic patients. Prospective study on adults with sepsis in the Emergency Department (ED). Area under the Receiver operator characteristic curve (AUC) was calculated to assess how scores predict mortality at 30 and 60 days (d) and upon admission to Intensive care unit (ICU). Among 469 patients, mortality was associated with higher SOFA, qSOFA, MEWS and lactates level. ICU admission was associated with higher SOFA, procalcitonin and MEWS. Prognostic performance for mortality were: SOFA AUC 30 d 0.76 (0.69-0.81); 60 d 0.74 (0.68-0.79); qSOFA AUC 30 d 0.72 (0.66-0.79); 60 d 0.73 (0.67-0.78) and lactates AUC 30 d 0.71 (0.60-0.82); 60d 0.65 (0.54- 0.73). For the outcome ICU admission, procalcitonin had the highest AUC [0.66 (0.56-0.64], followed by SOFA [0.61 (0.54-0.69)] and MEWS [0.60 (0.53-0.67)]. SOFA, qSOFA and lactates assessment after arrival in the ED have a good performance in detecting patients at risk of mortality for sepsis. Procalcitonin is useful to select patients that will need ICU admission.

Driving Pressure is Not Predictive of ARDS Outcome in Chest Trauma Patients Under Mechanical Ventilation

2021 ◽  
Author(s):  
severin ramin ◽  
Matteo Arcelli ◽  
Karim Bouchdoug ◽  
Thomas Laumon ◽  
Camille Duflos ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Chest Trauma ◽  
Characteristic Curve ◽  
Group Versus ◽  
Receiver Operator Characteristic Curve ◽  
Driving Pressure ◽  
Trauma Patients ◽  
Medical Patients ◽  
Mechanical Ventilation Duration ◽  
The Relationship

Abstract Background: The relationship between the driving pressure of the respiratory system (ΔPrs) under mechanical ventilation and worse outcome has never been studied specifically in chest trauma patients. The objective of the present study was to assess in cases of chest trauma the relationship between ΔPrs and severity of acute respiratory distress syndrome (ARDS) or death and length of stay. Methods: A retrospective analysis of severe trauma patients (ISS >15) with chest injuries admitted to the Trauma Center from January 2010 to December 2018 was performed. Patients who received mechanical ventilation were included in our analysis. Mechanical ventilation parameters and ΔPrs were recorded during the stay in the intensive care unit. Association of ΔPrs and ARDS with mortality and outcomes was specifically studied at the onset of ARDS (ΔPrs-ARDS) by receiver operator characteristic curve analysis, Kaplan-Meier curves and multivariate analysis.Results: Among the 266 chest trauma patients studied, 194 (73%) developed ARDS. ΔPrs was significantly higher in the ARDS group versus in the no ARDS group (11.6±2.4 cm H2O vs 10.9±1.9 cm H2O, p=0.04). Among the patients with ARDS, no difference according to the duration of mechanical ventilation was found between the high ΔPrs group (ΔPrs-ARDS >14 cm H2O) and the low ΔPrs group (ΔPrs-ARDS ≤14 cm H2O), (p=0.75). ΔPrs-ARDS was not independently associated with the duration of mechanical ventilation (hazard ratio [HR], 1.006; 95% CI, 0.95–1.07; p=0.8) or mortality (HR, 1.07; 95% CI, 0.9–1.28; p=0.45).Conclusion: A high ΔPrs-ARDS was not significantly associated with an increase in mechanical ventilation duration or mortality risk in ARDS patients with chest trauma in contrast with medical patients.

AVALIAÇÃO DE UM INSTRUMENTO DE MENSURAÇÃO DE MORBIDADE PSÍQUICA: ESTUDO DE VALIDAÇÃO DO SELF-REPORTING QUESTIONNAIRE (SRQ-20)

1970 ◽  
Vol 34 (3) ◽  
pp. 544 ◽  
Author(s):  
Kionna Oliveira Bernardes Santos ◽  
Tânia Maria de Araújo ◽  
Paloma de Sousa Pinho ◽  
Ana Cláudia Conceição Silva
Keyword(s):  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Clinical Interview ◽  
Receiver Operator Characteristic ◽  
Interview Schedule ◽  
Para Determinar ◽  
Self Reporting Questionnaire

O Self-Reporting Questionnaire (SRQ-20), desenvolvido pela Organização Mundial de Saúde, tem sido utilizado para mensuração de nível de suspeição de transtornos mentais em estudos brasileiros, especialmente em grupos de trabalhadores. O objetivo deste estudo foi avaliar o desempenho do SRQ-20, com base em indicadores de validade (sensibilidade, especificidade, taxa de classificação incorreta e valores preditivos), e determinar o melhor ponto de corte para classificação dos transtornos mentais comuns na população estudada. O estudo incluiu 91 indivíduos selecionados aleatoriamente de um estudo de corte transversal realizado com população residente em áreas urbanas de Feira de Santana (BA). Entrevistas clínicas, realizadas por psicólogas, utilizando o Revised Clinical Interview Schedule (CIS-R), foi adotada como padrão-ouro. Na avaliação do desempenho do SRQ-20 foram estimados indicadores de validade (sensibilidade e especificidade). A curva Receiver Operator Characteristic Curve (ROC) foi utilizada para determinar o melhor ponto de corte para classificação de suspeitos/não suspeitos. O ponto de corte de melhor desempenho foi de 6/7 para a população investigada, revelando desempenho razoável com área sob a curva de 0,789. Os resultados indicam que o SRQ-20 possui característica discriminante regular.

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