Clinical Examination for the Prediction of Mortality in the Critically Ill: The Simple Intensive Care Studies-I

Hiemstra, Bart; Eck, Ruben J.; Wiersema, Renske; Kaufmann, Thomas; Koster, Geert; Scheeren, Thomas; Snieder, Harold; Perner, Anders; Pettilä, Ville; Wetterslev, Jørn; Keus, Frederik; Horst, Iwan C. C. van der

doi:10.1097/ccm.0000000000003897

Cited by 18 publications

(11 citation statements)

References 43 publications

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“…The scientific evaluation in 50 adult critically ill patients hospitalized in CCU had a reasonable mortality precision. Like a recent study, we found that the fatal group was of a substantially higher age [ 10 ], and the death is significantly (p<0.001) higher within the first 24 hours in the elders [ 11 - 12 ], as mentioned in some reviews.…”

Section: Discussionsupporting

confidence: 84%

Assessment of the Clinical Interpreter of Death in Life-Threatening Infective Cases Admitted in the Intensive Care Unit of a North-Eastern State of India

Borah¹,

Saloi²,

Deka³

et al. 2021

Cureus

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The clinical factors affecting a patient's condition monitored over time could be useful not only to decide on an intervention that may increase the patients' possibilities of survival but also to predict the treatment outcome. Therefore, this study evaluates the clinical factors as predictors of mortality among severe sepsis patients admitted in the intensive care unit (ICU) of a tertiary care center. Method We did a prospective study on over 50 life-threatening infective cases with different causes admitted in the ICU. Clinical and biochemical parameters like temperature, heart rate, blood pressure, bicarbonate levels, blood lactate levels, and pH were monitored at admission, after 24 hours, and after 72 hours. The statistical analysis was done using Microsoft Excel (Microsoft Corporation, Redmond, WA) and the Statistical Package for the Social Studies (SPSS) version 22 (IBM Corp., Armonk, NY). We have obtained ethical clearance from the ethics committee (human) of Assam Medical College and Hospital, Dibrugarh. Before the collection of the data, we also took informed consent from the participants. Results The mean age of non-survivors was 44.35±11.64 years and that of survivors was 36.60±9.28 years, and the difference was statistically significant (p-value <0.003). An analysis of values of the various vital signs indicated substantial differences in the mean at different time intervals among survivors and non-survivors (p-value <0.05). Among non-survivors, mean temperature, pulse, and rate of respiration were observed to increase over time while blood pressure and oxygen saturation levels were significantly decreasing. Compared to survivors, the mean lactate levels of non-survivors at different time intervals were statistically significant (p-value <0.05). It is also observed that the pH of non-survivors was lower than survivors, and the mean pH value significantly different at different time intervals among the two groups (p-value <0.05). Conclusion The temperature, pulse, rate of respiration, blood pressure, and oxygen saturation levels are essential determinants of patient mortality in those suffering from a severe infection, besides serial lactate levels, bicarbonate levels, and pH levels.

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Section: Discussionsupporting

confidence: 84%

Assessment of the Clinical Interpreter of Death in Life-Threatening Infective Cases Admitted in the Intensive Care Unit of a North-Eastern State of India

Borah¹,

Saloi²,

Deka³

et al. 2021

Cureus

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“…In the FINNAKI cohort no imputations were performed. In the SICS-I cohort, predictor variables were imputed using multiple imputations, but data on Cr, urine output, and mortality were not imputed [19]. We appreciated missing Cr and urine output data by only calculating the AKI burden as a proportion of data which were available (i.e., neglecting missing data) (Table 1).…”

Section: Methodsmentioning

confidence: 99%

“…The original model of FINNAKI included age, presence of chronic liver failure, malignancy, arteriosclerosis, diabetes mellitus, systolic heart failure, or chronic immunosuppression, pre-morbid functional status (regarding daily activities), as well as presence of hypotension or resuscitation prior to ICU admission and ICU admission type [20]. The original model of SICS-I included age, vasopressor dose, respiratory rate, atrial fibrillation, systolic and diastolic blood pressure, level of consciousness following the alert, verbal, pain or unresponsive (AVPU) score, central temperature, and mottling rate scored on the knee (obtained during a one-time examination within 24 h of admission) [19].…”

Section: Methodsmentioning

confidence: 99%

Burden of acute kidney injury and 90-day mortality in critically ill patients

et al. 2019

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BackgroundMortality rates associated with acute kidney injury (AKI) vary among critically ill patients. Outcomes are often not corrected for severity or duration of AKI. Our objective was to analyse whether a new variable, AKI burden, would outperform 1) presence of AKI, 2) highest AKI stage, or 3) AKI duration in predicting 90-day mortality.MethodsKidney Diseases: Improving Global Outcomes (KDIGO) criteria using creatinine, urine output and renal replacement therapy were used to diagnose AKI. AKI burden was defined as AKI stage multiplied with the number of days that each stage was present (maximum five), divided by the maximum possible score yielding a proportion. The AKI burden as a predictor of 90-day mortality was assessed in two independent cohorts (Finnish Acute Kidney Injury, FINNAKI and Simple Intensive Care Studies I, SICS-I) by comparing four multivariate logistic regression models that respectively incorporated either the presence of AKI, the highest AKI stage, the duration of AKI, or the AKI burden.ResultsIn the FINNAKI cohort 1096 of 2809 patients (39%) had AKI and 90-day mortality of the cohort was 23%. Median AKI burden was 0.17 (IQR 0.07–0.50), 1.0 being the maximum. The model including AKI burden (area under the receiver operator curve (AUROC) 0.78, 0.76–0.80) outperformed the models using AKI presence (AUROC 0.77, 0.75–0.79, p = 0.026) or AKI severity (AUROC 0.77, 0.75–0.79, p = 0.012), but not AKI duration (AUROC 0.77, 0.75–0.79, p = 0.06). In the SICS-I, 603 of 1075 patients (56%) had AKI and 90-day mortality was 28%. Median AKI burden was 0.19 (IQR 0.08–0.46). The model using AKI burden performed better (AUROC 0.77, 0.74–0.80) than the models using AKI presence (AUROC 0.75, 0.71–0.78, p = 0.001), AKI severity (AUROC 0.76, 0.72–0.79. p = 0.008) or AKI duration (AUROC 0.76, 0.73–0.79, p = 0.009).ConclusionAKI burden, which appreciates both severity and duration of AKI, was superior to using only presence or the highest stage of AKI in predicting 90-day mortality. Using AKI burden or other more granular methods may be helpful in future epidemiological studies of AKI.

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“…The incremental value in knowing, and being able to follow, serum lactate would have been interesting (Casserly et al, 2015). However, it is notable that even in well-resourced settings, simple clinical assessment has been shown to be as reliable as the Acute Physiology and Chronic Health Evaluation-IV (APACHE-IV) and Simplified Acute Physiology Score-II (SAPS-II)-and superior to the SOFA score-in identifying patients at greatest risk of deterioration (Hiemstra et al, 2019). Indeed, even simpler clinical assessments, including the inability to walk, have been shown to have significant prognostic value in the LMIC setting (Kayambankadzanja et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

The comparative ability of commonly used disease severity scores to predict death or a requirement for ICU care in patients hospitalised with possible sepsis in Yangon, Myanmar

Minn

Aung

Kyaw

et al. 2021

International Journal of Infectious Diseases

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To determine the comparative prognostic utility of commonly used disease prediction scores in adults with presumed community-acquired sepsis in a resource-limited tropical setting. Methods: This prospective, observational study was performed on the medical ward of a tertiary-referral hospital in Yangon, Myanmar. The ability of the National Early Warning Score 2 (NEWS2), quick NEWS (qNEWS), quick Sequential Organ Failure Assessment (qSOFA) score, Universal Vital Assessment (UVA) and Sequential Organ Failure Assessment (SOFA) scores to predict a complicated inpatient course (death or requirement for intensive care unit (ICU) support) in patients with two or more systemic inflammatory response syndrome criteria was determined. Results: Among the 509 patients, 30 (6%) were HIV-seropositive. The most commonly confirmed diagnoses were tuberculosis (30/509, 5.9%) and measles (26/509, 5.1%). Overall, 75/509 (14.7%) died or required ICU support. All the scores except the qSOFA score, which was inferior, had a similar ability to predict a complicated inpatient course. Conclusions: In this resource-limited tropical setting, disease severity scores calculated at presentation using only vital signs-such as the NEWS2 score-identified high-risk sepsis patient as well as the SOFA score, which is calculated at 24 h and which also requires laboratory data. Use of these simple clinical scores can be used to facilitate recognition of the high-risk patient and to optimise the use of finite resources.

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Clinical Examination for the Prediction of Mortality in the Critically Ill: The Simple Intensive Care Studies-I

Abstract: Supplemental Digital Content is available in the text.

Cited by 18 publications

References 43 publications

Assessment of the Clinical Interpreter of Death in Life-Threatening Infective Cases Admitted in the Intensive Care Unit of a North-Eastern State of India

Assessment of the Clinical Interpreter of Death in Life-Threatening Infective Cases Admitted in the Intensive Care Unit of a North-Eastern State of India

Burden of acute kidney injury and 90-day mortality in critically ill patients

The comparative ability of commonly used disease severity scores to predict death or a requirement for ICU care in patients hospitalised with possible sepsis in Yangon, Myanmar

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