Standards for Frequency of Measurement and Documentation of Vital Signs and Physical Assessments

Schulman, Christine S.; Staul, LuAnn

doi:10.4037/ccn2010406

Cited by 20 publications

(11 citation statements)

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“…This suggests clinicians themselves should take a more active role in ensuring vital signs are measured and recorded at admission. There is debate over the frequency with which vital signs should be monitored particularly in critical care, telemetry units and progressive care [ 33 ] and the thresholds identifying potential concerns that should prompt action [ 9 , 18 , 20 , 34 ]. Such monitoring may help prevent deterioration and poor outcomes when interpreted carefully and combined with the review of clinical status that their performance encourages [ 15 , 16 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

An observational study of monitoring of vital signs in children admitted to Kenyan hospitals: an insight into the quality of nursing care?

Ogero

Ayieko

Makone

et al. 2018

Journal of Global Health

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BackgroundMeasurement and correct interpretation of vital signs is part of routine clinical care. Repeated measurement enhances early recognition of deterioration, may help prevent morbidity and mortality and is a standard of care in most countries.ObjectiveTo examine documentation of vital signs by clinicians for admissions to paediatric wards in Kenyan hospitals, to describe monitoring frequency by nurses and explore factors influencing frequency.MethodsVital signs information (temperature, respiratory and pulse rate) for the first 48 hours of admission was collected from case records of children admitted with non-surgical conditions to 13 Kenyan county hospitals between September 2013 and April 2016. A mixed effect negative binomial regression model was used to explore whether the severity of illness (indicated by danger signs or severe diagnostic episodes) is associated with increased vital signs observation frequency.ResultsWe examined 54 800 admission episodes with an overall mortality 6.1%. Nurse to bed ratios were very low (1:10 to 1:41 across hospitals). Admitting clinicians documented all or no vital signs in 57.0% and 8.4% cases respectively. For respiratory and pulse rates there was pronounced even end-digit preference (an indicator of incorrect information) and high frequency recording of specific values (P < 0.001) suggesting approximation. Monitoring frequency was explored in 41 738 children. Those with inpatient stays ≥48 hours were expected to have a vital signs count of 18, hospitals varied but most did not achieve this benchmark (median 9, range 2-30). There were clinically small but significant associations between vital signs count and presence of multiple severe illnesses or presence of severe pallor (adjusted relative risk ratio = 1.04, P < 0.01, 95% confidence interval CI = 1.02-1.06 and 1.05, P = 0.02, 95% CI = 1.01-1.09, respectively).ConclusionsData suggest accurate admission measures are sometimes missing especially for pulse and respiratory rates, possibly linked to manual measurement. Monitoring frequency is often low in the high risk population studied probably indicating how quality of nursing care is undermined by considerable human resource shortages.

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Section: Discussionmentioning

confidence: 99%

An observational study of monitoring of vital signs in children admitted to Kenyan hospitals: an insight into the quality of nursing care?

Ogero

Ayieko

Makone

et al. 2018

Journal of Global Health

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“…As patient monitoring strongly relies on clinical needs, no universal standards for ICU data completeness have been established [36-38]. However, a study by Frassica in 2005 [39] published a list of the top 80% of LTs common to all ICU patients within a university teaching hospital.…”

Section: Methodsmentioning

confidence: 99%

A New Insight Into Missing Data in Intensive Care Unit Patient Profiles: Observational Study

et al. 2019

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BackgroundThe data missing from patient profiles in intensive care units (ICUs) are substantial and unavoidable. However, this incompleteness is not always random or because of imperfections in the data collection process.ObjectiveThis study aimed to investigate the potential hidden information in data missing from electronic health records (EHRs) in an ICU and examine whether the presence or missingness of a variable itself can convey information about the patient health status.MethodsDaily retrieval of laboratory test (LT) measurements from the Medical Information Mart for Intensive Care III database was set as our reference for defining complete patient profiles. Missingness indicators were introduced as a way of representing presence or absence of the LTs in a patient profile. Thereafter, various feature selection methods (filter and embedded feature selection methods) were used to examine the predictive power of missingness indicators. Finally, a set of well-known prediction models (logistic regression [LR], decision tree, and random forest) were used to evaluate whether the absence status itself of a variable recording can provide predictive power. We also examined the utility of missingness indicators in improving predictive performance when used with observed laboratory measurements as model input. The outcome of interest was in-hospital mortality and mortality at 30 days after ICU discharge.ResultsRegardless of mortality type or ICU day, more than 40% of the predictors selected by feature selection methods were missingness indicators. Notably, employing missingness indicators as the only predictors achieved reasonable mortality prediction on all days and for all mortality types (for instance, in 30-day mortality prediction with LR, we achieved area under the curve of the receiver operating characteristic [AUROC] of 0.6836±0.012). Including indicators with observed measurements in the prediction models also improved the AUROC; the maximum improvement was 0.0426. Indicators also improved the AUROC for Simplified Acute Physiology Score II model—a well-known ICU severity of illness score—confirming the additive information of the indicators (AUROC of 0.8045±0.0109 for 30-day mortality prediction for LR).ConclusionsOur study demonstrated that the presence or absence of LT measurements is informative and can be considered a potential predictor of in-hospital and 30-day mortality. The comparative analysis of prediction models also showed statistically significant prediction improvement when indicators were included. Moreover, missing data might reflect the opinions of examining clinicians. Therefore, the absence of measurements can be informative in ICUs and has predictive power beyond the measured data themselves. This initial case study shows promise for more in-depth analysis of missing data and its informativeness in ICUs. Future studies are needed to generalize these results.

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“…However, in resource-rich settings, management of severe sepsis usually takes place in emergency departments or intensive care units where continuous monitoring is possible [9]. The initial management should be goal-directed and iterative, hence frequent monitoring is necessary to meet pre-set targets [1], [3].…”

Section: Introductionmentioning

confidence: 99%

Frequency of Vital Signs Monitoring and its Association with Mortality among Adults with Severe Sepsis Admitted to a General Medical Ward in Uganda

2014

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IntroductionOptimal vital signs monitoring of patients with severe sepsis in resource-limited settings may improve outcomes. The objective of this study was to determine the frequency of vital signs monitoring of patients with severe sepsis and its association with mortality in a regional referral hospital in Uganda.MethodsWe reviewed medical records of patients admitted to Mbarara Regional Referral Hospital in Southwestern Uganda with severe sepsis defined by the presence of infection plus ≥2 of the systemic inflammatory response syndrome criteria, and ≥1 organ dysfunction (altered mental state, hypotension, jaundice, or thrombocytopenia). We recorded frequency of vital signs monitoring in addition to socio-demographic, clinical, and outcome data. We analyzed the data using logistic regression.ResultsWe identified 202 patients with severe sepsis. The median age was 35 years (IQR, 25–47) and 98 (48%) were female. HIV infection and anemia was present in 115 (57%) and 83 (41%) patients respectively. There were 67 (33%) in-hospital deaths. The median monitoring frequency per day was 1.1 (IQR 0.9–1.5) for blood pressure, 1.0 (IQR, 0.8–1.3) for temperature and pulse, and 0.5 (IQR, 0.3–1.0) for respiratory rate. The frequency of vital signs monitoring decreased during the course of hospitalization. Patients who died had a higher frequency of vital signs monitoring (p<0.05). The admission respiratory rate was associated with both frequency of monitoring (coefficient of linear regression 0.6, 95% CI 0.5–0.8, p<0.001) and mortality (AOR 2.5, 95% CI 1.3–5.3, p = 0.01). Other predictors of mortality included severity of illness, HIV infection, and anemia (p<0.05).ConclusionsMore research is needed to determine the optimal frequency of vital signs monitoring for severely septic patients in resource-limited settings such as Uganda.

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Standards for Frequency of Measurement and Documentation of Vital Signs and Physical Assessments

Cited by 20 publications

References 1 publication

An observational study of monitoring of vital signs in children admitted to Kenyan hospitals: an insight into the quality of nursing care?

An observational study of monitoring of vital signs in children admitted to Kenyan hospitals: an insight into the quality of nursing care?

A New Insight Into Missing Data in Intensive Care Unit Patient Profiles: Observational Study

Frequency of Vital Signs Monitoring and its Association with Mortality among Adults with Severe Sepsis Admitted to a General Medical Ward in Uganda

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