Abnormal Admission Chest X-Ray and MEWS as ICU Outcome Predictors in a Sub-Saharan Tertiary Hospital: A Prospective Observational Study

Ssemmanda, Hannington; Luggya, Tonny Stone; Lubulwa, Clare; Muyinda, Zeridah; Kwitonda, Pascal; Wanzira, Humphrey; Ejoku, Joseph

doi:10.1155/2016/7134854

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…Louise indicated that adherence to the MEWS protocol is essential (2). Some studies have illustrated that the risk of death increased with an increasing MEWS score (3)(4)(5). However, Vorwerk voiced concerns about the tendency of low MEWS stratification to create type II errors (6).…”

Section: Introductionmentioning

confidence: 99%

Detection of deteriorating patients after Whipple surgery by a modified early warning score (MEWS)

Yu¹,

Huang²,

Liu³

et al. 2019

Ann. Transl. Med.

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Background: The modified early warning score (MEWS) was set up to supply prompt recognition of clinically deteriorating patients before they undergo a severe and life-threatening event. The study aimed to describe the probable usefulness of the MEWS in identifying deteriorating post-Whipple patients in hospital wards. Methods:We performed a study to analyze the relationship between the vital parameters and postoperative severe adverse events of patients after Whipple surgery in Guangdong Provincial People's Hospital from 2000 to 2017. In the retrospective study, a total of 13,651 sets of vital parameters in 236 Whipple postoperative patients were included. Subsequently, we applied a MEWS scoring system and explored the accuracy of the MEWS in evaluating the patients' final events versus advanced mathematical models. We then put the MEWS into the ward warning system and confirmed the accuracy of the MEWS based on the results of prospective studies again. Results:We assessed the ability of the MEWS to predict postoperative complications with an accuracy rate of 90.86-91.23%, a sensitivity of 83.04-90.88%, and a specificity of 90.85-95.73%. Conclusions:The MEWS model was applied to identify post-Whipple patients at risk of complication.Once the MEWS ≥2, interventions were needed to minimize the adverse events. Our data suggest that the MEWS is comparable to the advanced mathematical models, but MEWS is more accessible to perform and more generally applicable.

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Section: Introductionmentioning

confidence: 99%

Detection of deteriorating patients after Whipple surgery by a modified early warning score (MEWS)

Yu¹,

Huang²,

Liu³

et al. 2019

Ann. Transl. Med.

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“…Similarly, the tool could accurately identify low-risk and medium-risk patients to facilitate early caregiver intervention in patient rehabilitation [18]. There are several critical illness assessment tools, such as the Acute Physiology And Chronic Health Evaluation II (APACHE II) [19], Modi ed Early Warning Score (MEWS) [20,21], and Sequential Organ Failure Assessment (SOFA) [22,23]. APACHE II is unsuitable to evaluate patient condition and mortality risk in long-term hospitalization [24]; MEWS is more appropriate for evaluating patients in the general ward [20]; and both APACHE II and SOFA require blood tests, which prolongs evaluation.…”

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confidence: 99%

Development and clinical empirical validation of the chronic critical illness prognosis prediction model

Zhao

Wáng

et al. 2024

THC

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BACKGROUND: The evolution of critical care medicine and nursing has aided and enabled the rescue of a large number of patients from numerous life-threatening diseases. However, in many cases, patient health may not be quickly restored, and the long-term prognosis may not be optimistic. OBJECTIVES: In this study, we aimed to develop and validate a prediction model for accurate, precise, and objective identification of the severity of chronic critical illness (CCI) in patients. METHODS: We used a retrospective case-control and prospective cohort study with no interventions. Patients diagnosed with CCI admitted to the ICU of a large metropolitan public hospital were selected. In the case-control study, 344 patients (case: 172; control:172) were enrolled to develop the prognosis prediction model of chronic critical illness (PPCCI Model); 88 patients (case:44; control: 44) in a prospective cohort study, served as the validation cohort. The discrimination of the model was measured using the area under the curve (AUC) of the receiver operating characteristic curve (ROC). RESULTS: Age, prolonged mechanical ventilation (PMV), sepsis or other severe infections, Glasgow Coma Scale (GCS), mean artery pressure (MAP), heart rate (HR), respiratory rate (RR), oxygenation index (OI), and active bleeding were the nine predictors included in the model. In both cohorts, the PPCCI model outperformed the Acute Physiology And Chronic Health Evaluation II (APACHE II), Modified Early Warning Score (MEWS), and Sequential Organ Failure Assessment (SOFA) in identifying deceased patients with CCI (development cohort: AUC, 0.934; 95%CI, 0.908–0.960; validation cohort: AUC, 0.965; 95% CI, 0.931–0.999). CONCLUSION: The PPCCI model can provide ICU medical staff with a standardized measurement tool for assessing the condition of patients with CCI, enabling them to allocate ward monitoring resources rationally and communicate with family members.

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confidence: 99%

Development and clinical empirical validation of the Prognosis Prediction Model of Chronic Critical Illness

Zhao

Wáng

et al. 2020

Preprint

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Background A vast number of patients with chronic critical illness (CCI) have died of delayed organ failure in the intensive care unit (ICU). The weak organ function of patients needed appropriate tool to evaluate, which could provide reference for clinical decisions and communication with family members. The objective of this study was to develope and validate a prediction model for accurate, timely, simple, and objective identification of the critical degree of the patients' condition. Methods This study used a retrospective case–control and a prospective cohort study, with no interventions. Patients identified as CCI from a comprehensive ICU of a large metropolitan public hospital were selected. A total of 344 (case 172; control 172) patients were included to develop the Prognosis Prediction Model of Chronic Critical Illness (PPCCI Model) in this case-control study; 88 (case, 44; control 44) patients were included for the validation cohort in a prospective cohort study. The discrimination of the model was assessed by the area under the curve (AUC) of the receiver operating characteristic (ROC). Results The model comprised 9 predictors: age, prolonged mechanical ventilation (PMV), sepsis/other serious infections, Glasgow Coma Scale (GCS), mean artery pressure (MAP), heart rate (HR), respiratory rate (RR), oxygenation index (OI), and active bleeding.In both cohorts, the PPCCI Model could better identify the dead CCI patients (development cohort: AUC, 0.934; 95% CI, 0.908–0.960; validation cohort: AUC, 0.965; 95% CI, 0.931–0.999), and showed better discrimination than the Acute Physiology And Chronic Health Evaluation II (APACHE II), Modified Early Warning Score (MEWS), and Sequential Organ Failure Assessment (SOFA). Conclusions The PPCCI Model can provide a standardized measurement tool for ICU medical staff to evaluate the condition of CCI patients, to facilitate rational allocation of ward-monitoring resources or communicate with family members.

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Abnormal Admission Chest X-Ray and MEWS as ICU Outcome Predictors in a Sub-Saharan Tertiary Hospital: A Prospective Observational Study

Cited by 3 publications

References 22 publications

Detection of deteriorating patients after Whipple surgery by a modified early warning score (MEWS)

Detection of deteriorating patients after Whipple surgery by a modified early warning score (MEWS)

Development and clinical empirical validation of the chronic critical illness prognosis prediction model

Development and clinical empirical validation of the Prognosis Prediction Model of Chronic Critical Illness

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