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Systematic review shows nurse-led predictive symptom monitoring using biomarkers and clinical judgment curbs oncology palliative emergencies

Palliative & Supportive Care (PubMed)Oct 9, 2026

AI-summarized from the linked source. Educational brief, not medical advice.

Brief summary

A systematic review of 14 studies found that nurse-led predictive symptom management combining routine laboratory biomarkers with clinical nursing assessment enabled proactive intervention, decreased emergency department visits, and enhanced symptom control in cancer palliative care.

What NurseJet pulled from the source

Symptom distress in advanced cancer is traditionally addressed reactively after patients report severe breakthrough pain, delirium, or severe fatigue, frequently culminating in emergency department visits and crisis hospitalizations. This PRISMA systematic review (PROSPERO CRD420261432500) synthesized 14 clinical studies—including randomized controlled trials, cohort investigations, predictive modeling, and telehealth interventions—evaluating nurse-led predictive strategies in oncology palliative care. The synthesized evidence demonstrated that monitoring objective circulating biomarkers—notably C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), serum albumin, ferritin, and electrolyte derangements—predicted impending clinical deterioration and symptom crises. When bedside and ambulatory oncology nurses paired these biomarker trends with systematic clinical judgment, patients experienced superior symptom control, fewer acute emergency department visits, and more consistent care continuity.

Why this matters for nurses

Oncology and palliative care nurses frequently observe subtle shifts in patient functional status before acute decompensation occurs. Validating that nurses can pair objective inflammatory and nutritional biomarkers with clinical assessment transforms oncology nursing from reactive symptom relief into proactive crisis prevention, sparing fragile patients avoidable emergency room transfers.

Bedside takeaway

A systematic review of 14 studies found nurse-led predictive symptom management integrating biomarkers (CRP, NLR, albumin) with clinical judgment reduced ED visits and prevented acute decline in oncology palliative care.

Key takeaways

Systematic review synthesized 14 clinical studies evaluating nurse-led predictive symptom management protocols in oncology palliative care.
Laboratory biomarkers including CRP, neutrophil-lymphocyte ratio (NLR), albumin, ferritin, and electrolytes reliably heralded impending clinical decline.
Nurse-coordinated integration of biomarker trends with structured clinical assessment shifted care from reactive firefighting to proactive symptom stabilization.
Predictive nursing models were associated with reduced emergency department admissions and improved symptom-related quality of life.

Practice implications

Review trending laboratory indicators (such as rising CRP, worsening NLR, or falling albumin) during oncology and palliative encounters as early alert signals for impending symptom crisis or disease progression, adjusting supportive care plans and symptom titration before severe functional decline develops.

Limitations & cautions

Heterogeneity in study designs, biomarker panels, and tumor types across the 14 included studies; algorithms and predictive models require standardized electronic health record integration before universal bedside adoption.
AI-summarized from the linked source. Review the original article before applying to practice.

Citations

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Palliative & Supportive Care (PubMed)

Palliative & Supportive Care (PubMed). Nursing-led predictive symptom management in oncology palliative care: Using laboratory indicators and clinical judgment to improve patient quality of life: A systematic review.

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https://pubmed.ncbi.nlm.nih.gov/42850936/

Professional education only

This summary does not replace clinical judgment, facility policy, provider orders, or official guidelines. Verify practice changes against the original source and local protocol.

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Anesthesia and analgesia (PubMed)Oct 16, 2026Systematic Review

A meta-analysis of 25 randomized controlled trials with 3,344 surgical patients found perioperative ketamine and esketamine were associated with reduced postoperative delirium between days 2 and 7, but significantly increased postoperative dizziness.

Geriatrics Med-SurgAI summaryReview source

Systematic ReviewResearchMedication SafetyAI summary · original source linked below

Postoperative delirium (POD) is a common, distressing complication in older surgical patients, yet effective pharmacologic prevention options remain limited. This systematic review and meta-analysis of 25 randomized controlled trials (RCTs) involving 3,344 patients evaluated perioperative administration of ketamine (9 trials) and esketamine (16 trials) compared to placebo or standard care. Both agents were associated with reduced POD incidence (ketamine: risk ratio [RR] 0.69; 95% CI, 0.49–0.98; P = .04; esketamine: RR 0.58; 95% CI, 0.36–0.95; P = .03). Risk reductions were most evident with higher-dose regimens (single bolus: ketamine >0.5 mg/kg or esketamine >0.25 mg/kg; cumulative dose: ketamine >1.0 mg/kg or esketamine >0.5 mg/kg), bolus-plus-infusion administration, and in major surgical procedures. However, the preventative association was present only when delirium was assessed between postoperative days 2 and 7, not within the first 24 hours. Furthermore, ketamine/esketamine significantly increased the risk of postoperative dizziness (RR 1.45; 95% CI, 1.14–1.84; P = .002). Trial sequential analysis indicated that total evidence remains inconclusive due to insufficient overall sample size.

Key takeaways

Meta-analysis analyzed 25 RCTs with 3,344 patients receiving perioperative ketamine (9 trials) or esketamine (16 trials).
Both agents were associated with lower POD incidence versus control: ketamine RR 0.69 (P = .04) and esketamine RR 0.58 (P = .03).
Delirium reduction was apparent at postoperative days 2 to 7, but not during the initial 24 hours.
Dizziness risk was significantly elevated in treated patients (RR 1.45; 95% CI, 1.14–1.84; P = .002), and trial sequential analysis found cumulative sample size insufficient to draw definitive conclusions.

Why this matters on shift

Surgical and post-anesthesia nurses monitor older adults recovering from general anesthesia for confusion, agitation, and fall risk. While ketamine and esketamine show potential neurocognitive protective signals beyond day 1, their heightened dizziness profile increases post-op balance instability, requiring cautious bedside mobility precautions rather than unreserved clinical adoption.

Bedside takeaway

In a meta-analysis of 25 RCTs (3,344 patients), perioperative ketamine (RR 0.69) and esketamine (RR 0.58) reduced delirium at days 2–7 but significantly increased dizziness (RR 1.45).

Nursing implications

Screen surgical patients systematically for emergence and delayed delirium using validated tools (such as CAM or CAM-ICU) through postoperative day 7, implement strict fall and dizziness precautions when ketamine derivatives are administered, and avoid assuming early alertness guarantees absence of delayed delirium.

Limitations & cautions

Trial sequential analysis confirmed current evidence remains inconclusive due to inadequate total information size; dizziness was increased (RR 1.45); high heterogeneity in surgical populations and anesthesia protocols.
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Citations · exact source links

Anesthesia and analgesia (PubMed). Effects of Ketamine and Esketamine on Postoperative Delirium After General Anesthesia: A Meta-Analysis and Meta-Regression Analysis.

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Stroke (PubMed)Oct 1, 2026Clinical Guideline

The American Heart Association and American Stroke Association released updated 2026 guidelines for adult stroke rehabilitation and recovery, updating guidance across medical comorbidities, bone health, fall risk, and technology-assisted therapy.

Emergency Med-SurgAI summaryReview source

Clinical GuidelineGuidelineQuality ImprovementWorkflowAI summary · original source linked below

Replacing the previous 2016 recommendations, the American Heart Association and American Stroke Association published the comprehensive 2026 Guideline for Adult Stroke Rehabilitation and Recovery following extensive systematic review of literature published through late 2025. Directed toward interprofessional care teams, rehabilitation nurses, therapists, and family caregivers, the updated guideline establishes evidence-based standards spanning the inpatient acute-to-community recovery continuum. Major updates focus on the comprehensive assessment and management of vascular and medical comorbidities, structured fracture risk assessment, protocolized inpatient and home fall prevention, technology-enabled rehabilitation modalities, dedicated caregiver psychosocial support, community participation, and standardized evaluations for safe return to driving and employment.

Key takeaways

AHA/ASA 2026 guideline replaces 2016 standards across the inpatient and outpatient stroke rehabilitation continuum.
Establishes new recommendations for managing post-stroke medical comorbidities, bone mineral density loss, and fracture prevention.
Prioritizes structured fall prevention protocols and evaluates technology-enabled rehabilitation systems.
Expands dedicated guidance on family caregiver support, functional community participation, and safe return to driving.

Why this matters on shift

Neuroscience and medical-surgical nurses manage acute stroke survivors during the highest-risk window for post-stroke complications, including immobility, deconditioning, and falls. Updated national guidance gives bedside nurses clear authority to initiate early mobility safety assessments, safeguard bone health, and prepare family caregivers for post-discharge recovery demands.

Bedside takeaway

AHA/ASA updated its adult stroke rehabilitation guideline, emphasizing comorbidity management, bone health, structured fall prevention, technology-enabled therapies, and caregiver support.

Nursing implications

Incorporate structured fall risk assessments and fracture prevention strategies into post-stroke care plans, assess family caregiver burden early in the hospital stay, and coordinate closely with physical and occupational therapy on mobility goals and discharge equipment needs.

Limitations & cautions

Comprehensive guideline covers broad multi-domain stroke rehabilitation; the authors emphasize that substantial gaps in high-certainty evidence remain for specific neuro-recovery interventions, underscoring the need for ongoing randomized trials.
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Citations · exact source links

Stroke (PubMed). 2026 Guideline for Adult Stroke Rehabilitation and Recovery: A Guideline From the American Heart Association and American Stroke Association.

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Worldviews on evidence-based nursing (PubMed)Oct 1, 2026Systematic Review

A systematic review and meta-analysis of hospital sensor-based fall prevention systems found no significant overall reduction in falls or injuries, but machine-learning systems and mandatory implementation significantly lowered fall rates.

Med-Surg GeriatricsAI summaryReview source

Systematic ReviewResearchQuality ImprovementWorkflowAI summary · original source linked below

Hospital falls remain a pervasive cause of patient injury, extended length of stay, and nursing alarm fatigue. This systematic review and meta-analysis synthesized evidence from 16 interventional studies (seven pooled in meta-analysis) across MEDLINE, Embase, CINAHL, CENTRAL, and Web of Science evaluating sensor-based fall prevention technologies in hospital units. Using random-effects models with the Paule-Mandel estimator and Hartung-Knapp-Sidik-Jonkman adjustment, sensor systems overall demonstrated no statistically significant reduction in fall rates (incidence rate ratio [IRR] 0.77; 95% CI 0.38–1.58; I² = 92%), number of fallers (odds ratio 1.24; 95% CI 0.76–2.02; I² = 73%), or fall-related injuries (IRR 0.98; 95% CI 0.81–1.18; I² = 0%). However, subgroup analysis revealed that advanced machine learning-based sensor systems significantly reduced fall rates (IRR 0.50; 95% CI 0.32–0.79), whereas conventional rule-based sensors showed no effect. Mandatory unit-wide implementation was also associated with significantly reduced fall rates (IRR 0.51; 95% CI 0.37–0.70). Overall GRADE evidence certainty ranged from very low to low.

Key takeaways

Meta-analysis of 16 hospital studies found sensor-based fall systems overall did not significantly reduce fall rates (IRR 0.77) or injuries (IRR 0.98).
Machine learning-based sensor systems significantly cut inpatient fall rates by 50% (IRR 0.50, 95% CI 0.32–0.79).
Traditional rule-based pressure sensors showed no significant benefit on fall incidence.
Mandatory unit-wide sensor implementation was associated with a significant fall rate reduction (IRR 0.51, 95% CI 0.37–0.70), but not fewer fallers.

Why this matters on shift

Hospital bed and chair alarms are ubiquitous on med-surg and geriatric units, but frequently generate high false-alarm burdens and alert fatigue without clearly preventing patient harm. Demonstrating that conventional sensors fail to reduce falls while AI/machine-learning systems and unit-wide compliance halve fall rates informs hospital technology investments and nursing safety protocols.

Bedside takeaway

A meta-analysis found hospital sensor alarms overall did not reduce falls (IRR 0.77) or injuries (IRR 0.98), though machine-learning sensors significantly reduced fall rates by 50% (IRR 0.50).

Nursing implications

Do not rely on standard pressure sensor alarms as standalone fall prevention; pair technology with intentional rounding, assisted toileting, and environmental safety checks. When modern predictive or machine-learning sensors are deployed, adhere strictly to unit operational protocols to realize preventative benefits.

Limitations & cautions

High statistical heterogeneity among included trials (I² up to 92%); only seven studies eligible for statistical pooling; overall certainty of evidence rated very low to low under GRADE criteria.
AI-summarized from the linked source. Review the original article before applying to practice.

Citations · exact source links

Worldviews on evidence-based nursing (PubMed). Effectiveness of Sensor-Based Fall Prevention Systems in Hospitals: A Systematic Review and Meta-Analysis.

Open original sourceFull brief

AI-summarized from the linked source. Review the original article before applying to practice.

Key takeaways

Practice implications

Limitations & cautions

Exact source links

More updates for this clinical area

Meta-analysis of 25 RCTs finds perioperative ketamine and esketamine reduce postoperative delirium risk but increase dizziness

AHA/ASA releases 2026 adult stroke rehabilitation and recovery guideline across medical comorbidities and fall prevention

Meta-analysis of hospital sensor fall systems finds no overall fall reduction, but machine-learning sensors cut rates by 50%