Surgical Outcomes and Quality Improvement in Geriatric Patients

The global population is ageing rapidly, and older adults now represent an expanding cohort of surgical patients. Compared with younger counterparts, geriatric patients face higher rates of postoperative complications, prolonged hospital stay and mortality. Chronological age alone proves a poor predictor of outcome; instead, multidimensional assessments that capture physiological reserve, comorbidity burden and functional status have emerged as superior risk stratifiers. Frailty screening, comorbidity indices and standardised classifications of pre-operative status facilitate individualised care planning. In parallel, quality improvement initiatives—ranging from evidence-based care bundles in emergency surgery to machine-learning models for risk prediction and natural language processing to harmonise clinician assessments—have demonstrated reductions in mortality, complication rates and readmissions. Multidisciplinary collaboration among surgeons, anaesthetists, geriatricians and allied health professionals underpins perioperative optimisation, driving safer pathways and preserving recovery of independence in this vulnerable group.

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Surgical Outcomes and Quality Improvement in Geriatric Patients publication trend

The graph below shows the total number of articles in surgical outcomes and quality improvement in geriatric patients across all publications each year (not limited to Nature Index journals).

Technical terms

Frailty: A multidimensional syndrome reflecting reduced physiological reserve and increased vulnerability to stressors, often quantified by indices combining physical performance, weight loss, exhaustion and comorbidities.

ASA Physical Status classification: A system for categorising a patient’s preoperative health from class I (healthy) to class V (moribund), intended to guide perioperative risk assessment.

Charlson Comorbidity Index: A weighted score that predicts ten-year mortality by assigning points to a range of chronic comorbid conditions, adjusted for age.

Natural language processing (NLP): A branch of artificial intelligence that analyses unstructured text data—such as clinical notes—to automate tasks like classification of patient status or extraction of risk factors.

Goal-directed fluid therapy: An intraoperative strategy that tailors intravenous fluid administration to real-time haemodynamic targets to optimise tissue perfusion and reduce complications.

References

1. Comparison of NLP machine learning models with human physicians for ASA Physical Status classification. npj Digital Medicine (2024).
2. Development and Validation of a Prognostic Classification Model Predicting Postoperative Adverse Outcomes in Older Surgical Patients Using a Machine Learning Algorithm: Retrospective Observational Network Study. Journal of Medical Internet Research (2023).
3. Assessing and managing frailty in emergency laparotomy: a WSES position paper. World Journal of Emergency Surgery (2023).