Does concurrent or previous illness accurately predict cardiac arrest survival?

Prognostic models for outcome prediction following in-hospital cardiac arrest using pre-arrest factors: a systematic review, meta-analysis and critical appraisal

BACKGROUND

Several prediction models of survival after in-hospital cardiac arrest (IHCA) have been published, but no overview of model performance and external validation exists. We performed a systematic review of the available prognostic models for outcome prediction of attempted resuscitation for IHCA using pre-arrest factors to enhance clinical decision-making through improved outcome prediction.

METHODS

This systematic review followed the CHARMS and PRISMA guidelines. Medline, Embase, Web of Science were searched up to October 2021. Studies developing, updating or validating a prediction model with pre-arrest factors for any potential clinical outcome of attempted resuscitation for IHCA were included. Studies were appraised critically according to the PROBAST checklist. A random-effects meta-analysis was performed to pool AUROC values of externally validated models.

RESULTS

Out of 2678 initial articles screened, 33 studies were included in this systematic review: 16 model development studies, 5 model updating studies and 12 model validation studies. The most frequently included pre-arrest factors included age, functional status, (metastatic) malignancy, heart disease, cerebrovascular events, respiratory, renal or hepatic insufficiency, hypotension and sepsis. Only six of the developed models have been independently validated in external populations. The GO-FAR score showed the best performance with a pooled AUROC of 0.78 (95% CI 0.69-0.85), versus 0.59 (95%CI 0.50-0.68) for the PAM and 0.62 (95% CI 0.49-0.74) for the PAR.

CONCLUSIONS

Several prognostic models for clinical outcome after attempted resuscitation for IHCA have been published. Most have a moderate risk of bias and have not been validated externally. The GO-FAR score showed the most acceptable performance. Future research should focus on updating existing models for use in clinical settings, specifically pre-arrest counselling. Systematic review registration PROSPERO CRD42021269235. Registered 21 July 2021.

Hospital medicine (Part 2): what would improve acute hospital care?

There are so many obvious delays and inefficiencies in our traditional system of acute hospital care; it is clear that if outcomes are to be improved prompt accurate assessment immediately followed by competent and efficient treatment is essential. Early warning scores (EWS) help detect acutely ill patients who are seriously ill and likely to deteriorate. However, it is not known if any EWS has universal applicability to all patient populations. The benefit of Rapid Response Systems (RRS) such as Medical Emergency Teams has yet to be proven, possibly because doctors and nurses are reluctant to call the RRS for help. Reconfiguration of care delivery in an Acute Medical Assessment Unit has been suggested as a "proactive" alternative to the "reactive" approach of RRS. This method ensures every patient is in an appropriate and safe environment from the moment of first contact with the hospital. Further research is needed into what interventions are most effective in preventing the deterioration and/or resuscitating seriously ill patients. Although physicians expert in hospital care decrease the cost and length of hospitalization without compromising outcomes hospital care will continue to be both expensive and potentially dangerous.

Prognostication--the lost skill of medicine

Making a prognosis is one of the primary functions of the medical profession. At the end of the nineteenth century prognostication took up approximately ten percent of medical textbooks, by 1970 this had fallen to nearly zero. Given medical technology's awesome ability to prolong the process and suffering of dying today's patients need to know their prognosis in order to make choices about their treatment options. Whilst precise predictions of the future are obviously not possible, relatively simple mathematical modelling techniques can make reasonable estimates of likely outcomes for individual patients. The life expectancy of a patient of any age with any illness can be estimated provided the disease-specific mortality of the illness is known. Decision analysis or logistic regression models can then be used to determine the risks and benefits of various treatment options. A patient's prognosis does not just depend on their age and primary diagnosis, but also on the severity of their illness, their functional capacity both prior to and during the illness and the number of co-morbidities also suffered from. Several predictive instruments have been developed to help simplify the prediction of the outcome of individual patients. There are conflicting reports on how these models compare with doctors' intuition--whatever their strengths and weaknesses it is unlikely that they worsen clinical judgement. Therefore, all doctors should become familiar with them and use them appropriately.